Sangkyun Lee

Orcid: 0000-0001-8415-6368

Affiliations:
  • Hanyang University, ERICA, Ansan, South Korea
  • TU Dortmund, Department of Computer Science, Germany (former)
  • University of Wisconsin-Madison, Department of Computer Sciences, WI, USA (PhD 2011)


According to our database1, Sangkyun Lee authored at least 27 papers between 2009 and 2024.

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Bibliography

2024
CODE-SMASH: Source-Code Vulnerability Detection Using Siamese and Multi-Level Neural Architecture.
IEEE Access, 2024

Similarity-Based Source Code Vulnerability Detection Leveraging Transformer Architecture: Harnessing Cross- Attention for Hierarchical Analysis.
IEEE Access, 2024

SwiftThief: Enhancing Query Efficiency of Model Stealing by Contrastive Learning.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

2022
Model Stealing Defense against Exploiting Information Leak through the Interpretation of Deep Neural Nets.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Libra-CAM: An Activation-Based Attribution Based on the Linear Approximation of Deep Neural Nets and Threshold Calibration.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021
Improving the Robustness of Model Compression by On-Manifold Adversarial Training.
Future Internet, 2021

Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data.
CoRR, 2021

Hunt for Unseen Intrusion: Multi-Head Self-Attention Neural Detector.
IEEE Access, 2021

2019
Structure Learning of Gaussian Markov Random Fields with False Discovery Rate Control.
Symmetry, 2019

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems.
CoRR, 2019

2016
Integer undirected graphical models for resource-constrained systems.
Neurocomputing, 2016

Knowledge Discovery from Complex High Dimensional Data.
Proceedings of the Solving Large Scale Learning Tasks. Challenges and Algorithms, 2016

Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with Ordered L1-Norm.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Signature Selection for Grouped Features with a Case Study on Exon Microarrays.
Proceedings of the Feature Selection for Data and Pattern Recognition, 2015

2014
Sparse Inverse Covariance Estimation for Graph Representation of Feature Structure.
Proceedings of the Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, 2014

The Integer Approximation of Undirected Graphical Models.
Proceedings of the ICPRAM 2014, 2014

Kernel Completion for Learning Consensus Support Vector Machines in Bandwidth-limited Sensor Networks.
Proceedings of the ICPRAM 2014, 2014

Kernel Matrix Completion for Learning Nearly Consensus Support Vector Machines.
Proceedings of the Pattern Recognition Applications and Methods, 2014

Characterization of Subgroup Patterns from Graphical Representation of Genomic Data.
Proceedings of the Brain Informatics and Health - International Conference, 2014

2013
Spatio-temporal random fields: compressible representation and distributed estimation.
Mach. Learn., 2013

2012
Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning.
J. Mach. Learn. Res., 2012

Separable Approximate Optimization of Support Vector Machines for Distributed Sensing.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Improving Confidence of Dual Averaging Stochastic Online Learning via Aggregation.
Proceedings of the KI 2012: Advances in Artificial Intelligence, 2012

ASSET: Approximate Stochastic Subgradient Estimation Training for Support Vector Machines.
Proceedings of the ICPRAM 2012, 2012

2011
Approximate Stochastic Subgradient Estimation Training for Support Vector Machines
CoRR, 2011

Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning.
Proceedings of the 28th International Conference on Machine Learning, 2011

2009
Decomposition Algorithms for Training Large-Scale Semiparametric Support Vector Machines.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009


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