Jaesik Choi

发表

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, 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

Automatic Generation of Probabilistic Programming from Time Series Data

Jaesik Choi, Anh Tong, 2016 .

Improving Imprecise Compressive Sensing Models

Jaesik Choi, Dongeun Lee, Rafael Lima, 2015, UAI.

Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior pdf

Jaesik Choi, Anh Tong, Toan Tran, 2020, ArXiv.

Semi-Local Structure Patterns for Robust Face Detection

Jaesik Choi, Gil-Jin Jang, Kyungjoong Jeong, 2015, IEEE Signal Processing Letters.

A Robot Learning to Play a Mobile Game Under Unknown Dynamics pdf

Jaesik Choi, Wonjun Yoon, Sol-A. Kim, 2016, ArXiv.

Dynamic Online Performance Optimization in Streaming Data Compression

Jaesik Choi, Dongeun Lee, Alex Sim, 2018, 2018 IEEE International Conference on Big Data (Big Data).

Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems pdf

Jaesik Choi, Anh Tong, 2020, ArXiv.

Relational Dynamic Bayesian Networks with Locally Exchangeable Measures

Jaesik Choi, Kejia Hu, Jaesik Choi, 2013 .

Facial Expression Recognition using Face Alignment and AdaBoost

Jaesik Choi, Gil-Jin Jang, Kyungjoong Jeong, 2014 .

Greedy Algorithms for Sequential Sensing Decisions

Jaesik Choi, Eyal Amir, Hannaneh Hajishirzi, 2009, IJCAI.

Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck pdf

Jaesik Choi, Thanh T. Nguyen, 2017, ArXiv.

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

Jaesik Choi, 2020, KDD.

Automatic Decomposition of Self-Triggering Kernels of Hawkes Processes pdf

Jaesik Choi, Rafael Lima, Jaesik Choi, 2017, ArXiv.

Discovering Latent Covariance Structures for Multiple Time Series

Jaesik Choi, Anh Tong, 2019, ICML.

Low-complexity compressive sensing with downsampling

Heonshik Shin, Jaesik Choi, Dongeun Lee, 2014, IEICE Electron. Express.

Discovering Explainable Latent Covariance Structure for Multiple Time Series

Jaesik Choi, Anh Tong, 2017 .

Best Predictive GLMM using LASSO with Application on High-Speed Network

Jaesik Choi, Alex Sim, Jiming Jiang, 2013 .

Learning the Group Structure of Deep Neural Networks with an Expectation Maximization Method

Jaesik Choi, Subin Yi, 2018, 2018 IEEE International Conference on Data Mining Workshops (ICDMW).

Fast Change Point Detection for electricity market analysis

Kesheng Wu, Jaesik Choi, Horst D. Simon, 2013, 2013 IEEE International Conference on Big Data.

Lifted Relational Variational Inference

Jaesik Choi, Eyal Amir, Eyal Amir, 2012, UAI.

The Automatic Statistician: A Relational Perspective pdf

Jaesik Choi, Yunseong Hwang, Jaesik Choi, 2015, ArXiv.

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

Jaesik Choi, Seong-Whan Lee, Woo-Jeoung Nam, 2020, AAAI.

Efficient Methods for Lifted Inference with Aggregate Factors

Jaesik Choi, Hung Hai Bui, Rodrigo de Salvo Braz, 2011, AAAI.

2P2-E21 High speed navigation of a mobile robot based on robot's experiences

Woojin Chung, Jaesik Choi, Seokgyu Kim, 2006 .

Implementation of End-to-End Training of Deep Visuomotor Policies for Manipulation of a Robotic Arm of Baxter Research Robot

Jaesik Choi, Rafael Lima, Seongun Kim, 2019 .

Parameter Estimation for Relational Kalman Filtering

Jaesik Choi, Eyal Amir, Albert J. Valocchi, 2014, AAAI Workshop: Statistical Relational Artificial Intelligence.

Best Predictive Generalized Linear Mixed Model with Predictive Lasso for High-Speed Network Data Analysis

Jaesik Choi, Alex Sim, Jiming Jiang, 2015 .

Combining planning and motion planning

Jaesik Choi, Eyal Amir, Eyal Amir, 2009, 2009 IEEE International Conference on Robotics and Automation.

Lifted Inference on Transitive Relations

Jaesik Choi, Eyal Amir, Wen Pu, 2013, StarAI@AAAI.

A spatio-temporal pyramid matching for video retrieval

Won Jong Jeon, Sang-Chul Lee, Jaesik Choi, 2013, Comput. Vis. Image Underst..

Visualization of Convolutional Neural Networks for Time Series Input Data

Jaesik Choi, Sohee Cho, 2020 .

Low complexity sensing for big spatio-temporal data

Jaesik Choi, Dongeun Lee, 2014, 2014 IEEE International Conference on Big Data (Big Data).

Parametric Information Bottleneck to \\Optimize Stochastic Neural Networks

Jaesik Choi, Thanh Nguyen, Jaesik Choi, 2018 .

SAIL (UNIST) team Participation in KBP 2016 Cold-Start Slot filling

Jaesik Choi, Jiyeon Han, Seongwoo Lim, 2016, TAC.

UNIST SAIL System for TAC 2017 Cold Start Slot Filling

Jaesik Choi, Soonjae Kwon, Sehyun Lee, 2017, TAC.

Improving Statistical Similarity Based Data Reduction for Non-Stationary Data

Kesheng Wu, Jaesik Choi, Dongeun Lee, 2017, SSDBM.

Markov Information Bottleneck to Improve Information Flow in Stochastic Neural Networks

Jaesik Choi, Thanh T. Nguyen, 2019, Entropy.

Grouped Convolutional Neural Networks for Multivariate Time Series pdf

Man-Ki Yoon, Jaesik Choi, Subin Yi, 2017, ArXiv.

Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Units pdf

Jaesik Choi, Ginkyeng Lee, Sohee Cho, 2020, ArXiv.

Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models

Jaesik Choi, Eyal Amir, Tianfang Xu, 2014, Ground water.

XAI—Explainable artificial intelligence

Guang-Zhong Yang, Jaesik Choi, David Gunning, 2019, Science Robotics.

A Study of Optimization Approach for GPS Anti-Jamming System's Integration on Military Aircraft Based on the Requirement of Capability

Jaesik Choi, Ki-Su Shin, Moon-gul Lee, 2015 .

Extracting Baseline Electricity Usage Using Gradient Tree Boosting

Kesheng Wu, Taehoon Kim, Jaesik Choi, 2015, 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity).

Expanding Statistical Similarity Based Data Reduction to Capture Diverse Patterns

Kesheng Wu, Jaesik Choi, Dongeun Lee, 2017, 2017 Data Compression Conference (DCC).

Learning Execution Contexts from System Call Distribution for Anomaly Detection in Smart Embedded System

Lui Sha, Man-Ki Yoon, Jaesik Choi, 2017, 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI).

Efficient navigation of mobile robot based on the robot's experience in human co-existing environment

Jae-Bok Song, Woojin Chung, Jaesik Choi, 2005 .

Global Deconvolutional Networks for Semantic Segmentation pdf

Jaesik Choi, Vladimir Nekrasov, Janghoon Ju, 2016, BMVC.

Novel Data Reduction Based on Statistical Similarity

Kesheng Wu, Jaesik Choi, Dongeun Lee, 2016, SSDBM.

Learning Exponential Random Graph Models

Jaesik Choi, Eyal Amir, Dorothy L. Espelage, 2013 .

Learning Execution Contexts from System Call Distributions for Intrusion Detection in Embedded Systems

Lui Sha, Man-Ki Yoon, Jaesik Choi, 2015 .

Searching for Topological Symmetry in Data Haystack pdf

Kallol Roy, Jaesik Choi, Anh Tong, 2016, ArXiv.

Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes

Jaesik Choi, Anh Tong, Jiyeon Han, 2019, IJCAI.

An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks pdf

Jaesik Choi, Haedong Jeong, Giyoung Jeon, 2019, AAAI.

Lifted Inference for Relational Hybrid Models

Jaesik Choi, Jaesik Choi, 2012 .

Improved Predictive Deep Temporal Neural Networks with Trend Filtering pdf

Jaesik Choi, Youngjin Park, Deokjun Eom, 2020, ArXiv.

Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series

Jaesik Choi, Anh Tong, Yunseong Hwang, 2016, ICML.

Memory Heat Map: Anomaly detection in real-time embedded systems using memory behavior

Lui Sha, Man-Ki Yoon, Jaesik Choi, 2015, 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC).

Realtime On-Road Vehicle Detection with Optical Flows and Haar-Like Feature Detectors

Jaesik Choi, Jaesik Choi, 2012 .

Learning Dynamic Compressive Sensing Models pdf

Jaesik Choi, Dongeun Lee, Jaesik Choi, 2015, ArXiv.

Lifted Relational Kalman Filtering

Jaesik Choi, Eyal Amir, Abner Guzmán-Rivera, 2011, IJCAI.

Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling

Jaesik Choi, Seong-Whan Lee, Sol-A. Kim, 2018, ICML.

Lifted Inference for Relational Continuous Models

David J. Hill, Jaesik Choi, Eyal Amir, 2010, Statistical Relational Artificial Intelligence.

IMPROVING GROUNDWATER FLOW MODEL PREDICTION USING COMPLEMENTARY DATA-DRIVEN MODELS

Jaesik Choi, Eyal Amir, Albert J. Valocchi, 2012 .

SecureCore: A multicore-based intrusion detection architecture for real-time embedded systems

Lui Sha, Man-Ki Yoon, Jaesik Choi, 2013, 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS).

An End-to-End Robot Architecture to Manipulate Non-Physical State Changes of Objects

Jaesik Choi, Wonjun Yoon, Sol-A Kim, 2016 .

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism pdf

Jaesik Choi, Seungmin Lee, Young-ri Choi, 2020, USENIX ATC.

Reading Documents for Bayesian Online Change Point Detection

Taehoon Kim, Jaesik Choi, Jaesik Choi, 2015, EMNLP.

Learning Relational Kalman Filtering

Jaesik Choi, Eyal Amir, Albert J. Valocchi, 2015, AAAI.

Factored Planning for Controlling a Robotic Arm

Jaesik Choi, Eyal Amir, 2006, AAAI Fall Symposium: Integrating Reasoning into Everyday Applications.

A Scalable and Flexible Repository for Big Sensor Data

Heonshik Shin, Jaesik Choi, Dongeun Lee, 2015, IEEE Sensors Journal.

Generative Design of Electromagnetic Structures Through Bayesian Learning

Ki Jin Han, Kallol Roy, Jaesik Choi, 2018, IEEE Transactions on Magnetics.

Safe Navigation of a Mobile Robot Considering Visibility of Environment

Woojin Chung, Jae-Bok Song, Jaesik Choi, 2009, IEEE Transactions on Industrial Electronics.

Learning Compressive Sensing Models for Big Spatio-Temporal Data

Jaesik Choi, Dongeun Lee, Jaesik Choi, 2015, SDM.

A Deterministic Partition Function Approximation for Exponential Random Graph Models

Jaesik Choi, Eyal Amir, Wen Pu, 2015, IJCAI.

IDEALEM: Statistical Similarity Based Data Reduction pdf

Kesheng Wu, Jaesik Choi, Dongeun Lee, 2019, ArXiv.

Intrusion Detection Using Execution Contexts Learned from System Call Distributions of Real-Time Embedded Systems pdf

Lui Sha, Man-Ki Yoon, Jaesik Choi, 2015, ArXiv.

Spatio-temporal pyramid matching for sports videos

Won Jong Jeon, Sang-Chul Lee, Jaesik Choi, 2008, MIR '08.

Factor-guided motion planning for a robot arm

Jaesik Choi, Eyal Amir, Eyal Amir, 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks pdf

Jaesik Choi, Haedong Jeong, Jiyeon Han, 2021, AAAI.

Lifted Aggregation and Skolemization for Directed Models

Guy Van den Broeck, Wannes Meert, Jaesik Choi, 2021, An Introduction to Lifted Probabilistic Inference.

Conditional Temporal Neural Processes with Covariance Loss

Jaesik Choi, Janghoon Ju, Boseon Yoo, 2021, ICML.

Automatic Correction of Internal Units in Generative Neural Networks

Jaesik Choi, Jiyeon Han, Haedong Jeong, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems

Jaesik Choi, Anh Tong, Jaesik Choi, 2020, AAAI.

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

Jaesik Choi, Ginkyeng Lee, Sohee Cho, 2021, KDD.

Lifted Inference for Hybrid Relational Models

Kristian Kersting, Jaesik Choi, Yuqiao Chen, 2021, An Introduction to Lifted Probabilistic Inference.

Hawkes Process Kernel Structure Parametric Search with Renormalization Factors

Jaesik Choi, Rafael Lima, 2018 .