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2020 – today
- 2025
[c71]Subeen Lee, Jiyeon Han, Soyeon Kim, Jaesik Choi:
Diverse Rare Sample Generation with Pretrained GANs. AAAI 2025: 4553-4561- [c70]Artyom Stitsyuk, Jaesik Choi:
xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition. AAAI 2025: 20601-20609 - [i57]Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Duc Nguyen, Toan M. Tran, David Hall, Cheongwoong Kang, Jaesik Choi:
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning. CoRR abs/2503.01329 (2025) - [i56]Youngju Joung, Sehyun Lee, Jaesik Choi:
Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information. CoRR abs/2503.09068 (2025) - [i55]Jiyeon Han, Dahee Kwon, Gayoung Lee, Junho Kim, Jaesik Choi:
Enhancing Creative Generation on Stable Diffusion-based Models. CoRR abs/2503.23538 (2025) - 2024
- [j10]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: 102301 (2024) - [c69]Jihyeon Seong, Jungmin Kim, Jaesik Choi:
Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings. AAAI 2024: 8948-8956 - [c68]Wonjoon Chang, Dahee Kwon, Jaesik Choi:
Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision. AAAI 2024: 11212-11220 - [c67]Seongwoo Lim, Won Jo, Joohyung Lee, Jaesik Choi:
Pathwise Explanation of ReLU Neural Networks. AISTATS 2024: 4645-4653 - [c66]Hyunkyung Han, Jihyeon Seong, Jaesik Choi:
CardioCaps: Attention-Based Capsule Network for Class-Imbalanced Echocardiogram Classification. BigComp 2024: 287-294 - [c65]Bumjin Park, Jaesik Choi:
Identifying the Source of Generation for Large Language Models. ICPRAI (2) 2024: 91-105 - [c64]Youngju Joung, Sehyun Lee, Jaesik Choi:
Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information. ICPRAI (1) 2024: 308-321 - [c63]Jihyeon Seong, Sekwang Oh, Jaesik Choi:
Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learning. IJCAI 2024: 2324-2332 - [c62]Bumjin Park, Jaesik Choi:
Memorizing Documents with Guidance in Large Language Models. IJCAI 2024: 6460-6468 - [i54]Hyunkyung Han, Jihyeon Seong, Jaesik Choi:
CardioCaps: Attention-based Capsule Network for Class-Imbalanced Echocardiogram Classification. CoRR abs/2403.09108 (2024) - [i53]Jihyeon Seong, Jungmin Kim, Jaesik Choi:
Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings. CoRR abs/2403.09749 (2024) - [i52]Soyeon Kim, Jihyeon Seong, Hyunkyung Han, Jaesik Choi:
Capsule Neural Networks as Noise Stabilizer for Time Series Data. CoRR abs/2403.13867 (2024) - [i51]Hyunkyung Han, Jaesik Choi:
Optimal path for Biomedical Text Summarization Using Pointer GPT. CoRR abs/2404.08654 (2024) - [i50]Jihyeon Seong, Sekwang Oh, Jaesik Choi:
Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learning. CoRR abs/2406.03665 (2024) - [i49]Bumjin Park, Jaesik Choi:
Memorizing Documents with Guidance in Large Language Models. CoRR abs/2406.15996 (2024) - [i48]Bumjin Park, Jaesik Choi:
Identifying the Source of Generation for Large Language Models. CoRR abs/2407.12846 (2024) - [i47]Artyom Stitsyuk, Jaesik Choi:
xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition. CoRR abs/2412.17323 (2024) - [i46]Subeen Lee, Jiyeon Han, Soyeon Kim, Jaesik Choi:
Diverse Rare Sample Generation with Pretrained GANs. CoRR abs/2412.19543 (2024) - 2023
- [c61]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: 339-355 - [c60]Cheongwoong Kang, Jaesik Choi:
Impact of Co-occurrence on Factual Knowledge of Large Language Models. EMNLP (Findings) 2023: 7721-7735 - [c59]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: 101-119 - [c58]Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Toan M. Tran, Jaesik Choi:
SigFormer: Signature Transformers for Deep Hedging. ICAIF 2023: 124-132 - [c57]Giyoung Jeon, Haedong Jeong, Jaesik Choi:
Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling. ICCV 2023: 2052-2061 - [c56]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 - [c55]Seongun Kim, Kyowoon Lee, Jaesik Choi:
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills. ICML 2023: 16668-16695 - [c54]Kyowoon Lee, Seongun Kim, Jaesik Choi:
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning. ICRA 2023: 1673-1679 - [c53]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: 87-92 - [c52]Kyowoon Lee, Seongun Kim, Jaesik Choi:
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans. NeurIPS 2023 - [i45]Nesma Mahmoud, Hanna Antson, Jaesik Choi, Osamu Shimmi, Kallol Roy:
Stress and Adaptation: Applying Anna Karenina Principle in Deep Learning for Image Classification. CoRR abs/2302.11380 (2023) - [i44]Inyoung Paik, Jaesik Choi:
The Disharmony Between BN and ReLU Causes Gradient Explosion, but is Offset by the Correlation Between Activations. CoRR abs/2304.11692 (2023) - [i43]Kyowoon Lee, Seongun Kim, Jaesik Choi:
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning. CoRR abs/2305.19746 (2023) - [i42]Cheongwoong Kang, Jaesik Choi:
Impact of Co-occurrence on Factual Knowledge of Large Language Models. CoRR abs/2310.08256 (2023) - [i41]Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Toan M. Tran, Jaesik Choi:
SigFormer: Signature Transformers for Deep Hedging. CoRR abs/2310.13369 (2023) - [i40]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 abs/2310.16095 (2023) - [i39]Seongun Kim, Kyowoon Lee, Jaesik Choi:
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills. CoRR abs/2310.19424 (2023) - [i38]Kyowoon Lee, Seongun Kim, Jaesik Choi:
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans. CoRR abs/2310.19427 (2023) - [i37]Seongun Kim, Jaesik Choi:
Explaining the Decisions of Deep Policy Networks for Robotic Manipulations. CoRR abs/2310.19432 (2023) - [i36]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 abs/2310.19775 (2023) - [i35]Wonjoon Chang, Dahee Kwon, Jaesik Choi:
Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision. CoRR abs/2312.17285 (2023) - 2022
- [j9]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): 150 (2022) - [j8]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): 2444-2453 (2022) - [c51]Haedong Jeong, Jiyeon Han, Jaesik Choi:
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks. AAAI 2022: 1052-1059 - [c50]Hwanil Choi, Wonjoon Chang, Jaesik Choi:
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks? IJCAI 2022: 2888-2894 - [c49]Giyoung Jeon, Haedong Jeong, Jaesik Choi:
Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network. NeurIPS 2022 - [c48]Anh Tong, Thanh Nguyen-Tang, Toan M. Tran, Jaesik Choi:
Learning Fractional White Noises in Neural Stochastic Differential Equations. NeurIPS 2022 - [i34]Hwanil Choi, Wonjoon Chang, Jaesik Choi:
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks? CoRR abs/2201.06346 (2022) - [i33]Deokjun Eom, Sehyun Lee, Jaesik Choi:
Variational Neural Temporal Point Process. CoRR abs/2202.10585 (2022) - [i32]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 abs/2206.08549 (2022) - [i31]Seongjin Park, Haedong Jeong, Giyoung Jeon, Jaesik Choi:
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network. CoRR abs/2207.03400 (2022) - [i30]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 abs/2209.00599 (2022) - [i29]Bumjin Park, Jaesik Choi:
Explanation on Pretraining Bias of Finetuned Vision Transformer. CoRR abs/2211.15428 (2022) - 2021
- [c47]Anh Tong, Toan M. Tran, Hung Bui, Jaesik Choi:
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior. AAAI 2021: 9906-9914 - [c46]Anh Tong, Jaesik Choi:
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems. AAAI 2021: 9915-9922 - [c45]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: 11604-11612 - [c44]Ali Tousi, Haedong Jeong, Jiyeon Han, Hwanil Choi, Jaesik Choi:
Automatic Correction of Internal Units in Generative Neural Networks. CVPR 2021: 7932-7940 - [c43]Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim, Jaesik Choi:
Conditional Temporal Neural Processes with Covariance Loss. ICML 2021: 12051-12061 - [c42]Seongun Kim, Jaesik Choi:
Explaining the Decisions of Deep Policy Networks for Robotic Manipulations. IROS 2021: 2663-2669 - [c41]Sohee Cho, Wonjoon Chang, Ginkyeng Lee, Jaesik Choi:
Interpreting Internal Activation Patterns in Deep Temporal Neural Networks by Finding Prototypes. KDD 2021: 158-166 - [i28]Ali Tousi, Haedong Jeong, Jiyeon Han, Hwanil Choi, Jaesik Choi:
Automatic Correction of Internal Units in Generative Neural Networks. CoRR abs/2104.06118 (2021) - [i27]Haedong Jeong, Jiyeon Han, Jaesik Choi:
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks. CoRR abs/2112.08814 (2021) - 2020
- [c40]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: 2501-2508 - [c39]Giyoung Jeon, Haedong Jeong, Jaesik Choi:
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks. AAAI 2020: 4288-4295 - [c38]Youngjin Park, Deokjun Eom, Byoungki Seo, Jaesik Choi:
Improved predictive deep temporal neural networks with trend filtering. ICAIF 2020: 8:1-8:8 - [c37]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: 7300-7306 - [c36]Jaesik Choi:
Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data. KDD 2020: 3563-3564 - [c35]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 ATC 2020: 307-321 - [i26]Sohee Cho, Ginkyeng Lee, Jaesik Choi:
Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Units. CoRR abs/2004.12538 (2020) - [i25]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 abs/2005.14038 (2020) - [i24]Youngjin Park, Deokjun Eom, Byoungki Seo, Jaesik Choi:
Improved Predictive Deep Temporal Neural Networks with Trend Filtering. CoRR abs/2010.08234 (2020) - [i23]Anh Tong, Jaesik Choi:
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems. CoRR abs/2010.09301 (2020) - [i22]Woo-Jeoung Nam, Jaesik Choi, Seong-Whan Lee:
Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations. CoRR abs/2012.03434 (2020) - [i21]Anh Tong, Toan M. Tran, Hung Bui, Jaesik Choi:
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior. CoRR abs/2012.11339 (2020)
2010 – 2019
- 2019
- [j7]Thanh T. Nguyen, Jaesik Choi:
Markov Information Bottleneck to Improve Information Flow in Stochastic Neural Networks. Entropy 21(10): 976 (2019) - [j6]David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, Guang-Zhong Yang:
XAI - Explainable artificial intelligence. Sci. Robotics 4(37) (2019) - [c34]Anh Tong, Jaesik Choi:
Discovering Latent Covariance Structures for Multiple Time Series. ICML 2019: 6285-6294 - [c33]Jiyeon Han, Kyowoon Lee, Anh Tong, Jaesik Choi:
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes. IJCAI 2019: 2449-2455 - [i20]Woo-Jeoung Nam, Jaesik Choi, Seong-Whan Lee:
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks. CoRR abs/1904.00605 (2019) - [i19]Jiyeon Han, Kyowoon Lee, Anh Tong, Jaesik Choi:
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes. CoRR abs/1905.13168 (2019) - [i18]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 abs/1909.07050 (2019) - [i17]Sunjae Kwon, Cheongwoong Kang, Jiyeon Han, Jaesik Choi:
Why Do Masked Neural Language Models Still Need Common Sense Knowledge? CoRR abs/1911.03024 (2019) - [i16]Dongeun Lee, Alex Sim, Jaesik Choi, Kesheng Wu:
IDEALEM: Statistical Similarity Based Data Reduction. CoRR abs/1911.06980 (2019) - [i15]Giyoung Jeon, Haedong Jeong, Jaesik Choi:
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks. CoRR abs/1912.05827 (2019) - 2018
- [j5]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): 3-20 (2018) - [c32]J. Kade Gibson, Dongeun Lee, Jaesik Choi, Alex Sim:
Dynamic Online Performance Optimization in Streaming Data Compression. IEEE BigData 2018: 534-541 - [c31]Subin Yi, Jaesik Choi:
Learning the Group Structure of Deep Neural Networks with an Expectation Maximization Method. ICDM Workshops 2018: 689-696 - [c30]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: 2943-2952 - 2017
- [c29]Dongeun Lee, Alex Sim, Jaesik Choi, Kesheng Wu:
Expanding Statistical Similarity Based Data Reduction to Capture Diverse Patterns. DCC 2017: 445 - [c28]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: 191-196 - [c27]Dongeun Lee, Alex Sim, Jaesik Choi, Kesheng Wu:
Improving Statistical Similarity Based Data Reduction for Non-Stationary Data. SSDBM 2017: 37:1-37:6 - [i14]Seongwoo Lim, Soonjae Kwon, Sehyun Lee, Jaesik Choi:
UNIST SAIL System for TAC 2017 Cold Start Slot Filling. TAC 2017 - [i13]Rafael Lima, Jaesik Choi:
Automatic Decomposition of Self-Triggering Kernels of Hawkes Processes. CoRR abs/1703.09068 (2017) - [i12]Subin Yi, Janghoon Ju, Man-Ki Yoon, Jaesik Choi:
Grouped Convolutional Neural Networks for Multivariate Time Series. CoRR abs/1703.09938 (2017) - [i11]Thanh T. Nguyen, Jaesik Choi:
Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck. CoRR abs/1712.01272 (2017) - 2016
- [c26]Vladimir Nekrasov, Janghoon Ju, Jaesik Choi:
Global Deconvolutional Networks for Semantic Segmentation. BMVC 2016 - [c25]Yunseong Hwang, Anh Tong, Jaesik Choi:
Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series. ICML 2016: 3030-3039 - [c24]Dongeun Lee, Alex Sim, Jaesik Choi, Kesheng Wu:
Novel Data Reduction Based on Statistical Similarity. SSDBM 2016: 21:1-21:12 - [c23]Dongeun Lee, Rafael Lima, Jaesik Choi:
Improving Imprecise Compressive Sensing Models. UAI 2016 - [i10]Seongwoo Lim, Jiyeon Han, Kyowoon Lee, Jaesik Choi:
SAIL (UNIST) team Participation in KBP 2016 Cold-Start Slot filling. TAC 2016 - [i9]Vladimir Nekrasov, Janghoon Ju, Jaesik Choi:
Global Deconvolutional Networks for Semantic Segmentation. CoRR abs/1602.03930 (2016) - [i8]Wonjun Yoon, Sol-A. Kim, Jaesik Choi:
A Robot Learning to Play a Mobile Game Under Unknown Dynamics. CoRR abs/1603.01303 (2016) - [i7]Kallol Roy, Anh Tong, Jaesik Choi:
Searching for Topological Symmetry in Data Haystack. CoRR abs/1603.03703 (2016) - 2015
- [j4]Kyungjoong Jeong, Jaesik Choi, Gil-Jin Jang:
Semi-Local Structure Patterns for Robust Face Detection. IEEE Signal Process. Lett. 22(9): 1400-1403 (2015) - [c22]Jaesik Choi, Eyal Amir, Tianfang Xu, Albert J. Valocchi:
Learning Relational Kalman Filtering. AAAI 2015: 2539-2546 - [c21]Man-Ki Yoon, Lui Sha, Sibin Mohan, Jaesik Choi:
Memory heat map: anomaly detection in real-time embedded systems using memory behavior. DAC 2015: 35:1-35:6 - [c20]Taehoon Kim, Jaesik Choi:
Reading Documents for Bayesian Online Change Point Detection. EMNLP 2015: 1610-1619 - [c19]Wen Pu, Jaesik Choi, Yunseong Hwang, Eyal Amir:
A Deterministic Partition Function Approximation for Exponential Random Graph Models. IJCAI 2015: 192-200 - [c18]Dongeun Lee, Jaesik Choi:
Learning Compressive Sensing Models for Big Spatio-Temporal Data. SDM 2015: 667-675 - [c17]Taehoon Kim, Dongeun Lee, Jaesik Choi, Anna Spurlock, Alex Sim, Annika Todd, Kesheng Wu:
Extracting Baseline Electricity Usage Using Gradient Tree Boosting. SmartCity 2015: 734-741 - [i6]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 abs/1501.05963 (2015) - [i5]Dongeun Lee, Jaesik Choi:
Learning Dynamic Compressive Sensing Models. CoRR abs/1502.04538 (2015) - [i4]Yunseong Hwang, Jaesik Choi:
The Automatic Statistician: A Relational Perspective. CoRR abs/1511.08343 (2015) - 2014
- [j3]Dongeun Lee, Jaesik Choi, Heonshik Shin:
Low-complexity compressive sensing with downsampling. IEICE Electron. Express 11(3): 20130947 (2014) - [c16]Jaesik Choi, Eyal Amir, Tianfang Xu, Albert J. Valocchi:
Parameter Estimation for Relational Kalman Filtering. StarAI@AAAI 2014 - [c15]Dongeun Lee, Jaesik Choi:
Low complexity sensing for big spatio-temporal data. IEEE BigData 2014: 323-328 - 2013
- [j2]Jaesik Choi, Ziyu Wang, Sang-Chul Lee, Won J. Jeon:
A spatio-temporal pyramid matching for video retrieval. Comput. Vis. Image Underst. 117(6): 660-669 (2013) - [c14]Wen Pu, Jaesik Choi, Eyal Amir:
Lifted Inference on Transitive Relations. StarAI@AAAI 2013 - [c13]William Gu, Jaesik Choi, Ming Gu, Horst D. Simon, Kesheng Wu:
Fast Change Point Detection for electricity market analysis. IEEE BigData 2013: 50-57 - [c12]Man-Ki Yoon, Sibin Mohan, Jaesik Choi, Jung-Eun Kim, Lui Sha:
SecureCore: A multicore-based intrusion detection architecture for real-time embedded systems. IEEE Real-Time and Embedded Technology and Applications Symposium 2013: 21-32 - 2012
- [b1]Jaesik Choi:
Lifted Inference for Relational Hybrid Models. University of Illinois Urbana-Champaign, USA, 2012 - [c11]Jaesik Choi, Eyal Amir:
Nonparametric Relational Hybrid Models. StarAI@UAI 2012 - [c10]Jaesik Choi, Eyal Amir:
Lifted Relational Variational Inference. UAI 2012: 196-206 - [i3]Jaesik Choi, Eyal Amir, David J. Hill:
Lifted Inference for Relational Continuous Models. CoRR abs/1203.3473 (2012) - [i2]Jaesik Choi, Eyal Amir:
Lifted Relational Variational Inference. CoRR abs/1210.4867 (2012) - 2011
- [c9]Jaesik Choi, Rodrigo de Salvo Braz, Hung Hai Bui:
Efficient Methods for Lifted Inference with Aggregate Factors. AAAI 2011: 1030-1036 - [c8]Jaesik Choi, Abner Guzmán-Rivera, Eyal Amir:
Lifted Relational Kalman Filtering. IJCAI 2011: 2092-2099 - 2010
- [c7]Jaesik Choi, David J. Hill, Eyal Amir:
Lifted Inference for Relational Continuous Models. StarAI@AAAI 2010 - [c6]Jaesik Choi, Eyal Amir, David J. Hill:
Lifted Inference for Relational Continuous Models. UAI 2010: 126-134 - [i1]Jaesik Choi, Eyal Amir:
Combining Planning and Motion Planning. Cognitive Robotics 2010
2000 – 2009
- 2009
- [j1]Woojin Chung, Seokgyu Kim, Minki Choi, Jaesik Choi, Hoyeon Kim, Chang-Bae Moon, Jae-Bok Song:
Safe Navigation of a Mobile Robot Considering Visibility of Environment. IEEE Trans. Ind. Electron. 56(10): 3941-3950 (2009) - [c5]Jaesik Choi, Eyal Amir:
Combining planning and motion planning. ICRA 2009: 238-244 - [c4]Hannaneh Hajishirzi, Afsaneh Shirazi, Jaesik Choi, Eyal Amir:
Greedy Algorithms for Sequential Sensing Decisions. IJCAI 2009: 1908-1915 - 2008
- [c3]Jaesik Choi, Won J. Jeon, Sang-Chul Lee:
Spatio-temporal pyramid matching for sports videos. Multimedia Information Retrieval 2008: 291-297 - 2007
- [c2]Jaesik Choi, Eyal Amir:
Factor-guided motion planning for a robot arm. IROS 2007: 27-32 - 2006
- [c1]Jaesik Choi, Eyal Amir:
Factored Planning for Controlling a Robotic Arm. AAAI Fall Symposium: Integrating Reasoning into Everyday Applications 2006: 9-14
Coauthor Index
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, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
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Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-04-22 21:07 CEST by the dblp team
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