Taesup Moon

发表

Dynamic functional connectivity analysis reveals improved association between brain networks and eating behaviors compared to static analysis

Hyunjin Park, Taesup Moon, Bo-yong Park, 2018, Behavioural Brain Research.

Exploiting User Preference for Online Learning in Web Content Optimization Systems

Yi Chang, Anlei Dong, Bo Long, 2014, TIST.

A Denoising Loss Bound for Neural Network based Universal Discrete Denoisers

Taesup Moon, 2017 .

Is Continual Learning Truly Learning Representations Continually?

Hyunwoo J. Kim, Moontae Lee, Honglak Lee, 2022, ArXiv.

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks

D. Wipf, Quan Gan, Taesup Moon, 2022, ArXiv.

Bandits with Generalized Linear Models

Wei Chu, John Langford, Lihong Li, 2012, ICML On-line Trading of Exploration and Exploitation.

SS-IL: Separated Softmax for Incremental Learning

Taesup Moon, Hongjoon Ahn, Jihwan Kwak, 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks

D. Wipf, Quan Gan, Taesup Moon, 2022, ArXiv.

Is Continual Learning Truly Learning Representations Continually?

Hyunwoo J. Kim, Moontae Lee, Honglak Lee, 2022, ArXiv.

GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training

Jianlong Fu, Taesup Moon, Jaeseok Byun, 2022, ECCV.

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks

D. Wipf, Quan Gan, Taesup Moon, 2022, NeurIPS.

GAN-Based Framework for Unified Estimation of Process-Induced Random Variation in FinFET

C. Shin, Taesup Moon, Sangho Yu, 2022, IEEE Access.

Interpretable deep learning‐based hippocampal sclerosis classification

J. Moon, Taesup Moon, Dohyun Kim, 2022, Epilepsia open.

Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel

Taesup Moon, Tae-Eon Park, Taesup Moon, 2020, AISTATS.

Is the posterior cingulate cortex an on-off switch for tinnitus?: A comparison between hearing loss subjects with and without tinnitus

D. De Ridder, S. Vanneste, B. Choi, 2021, Hearing Research.

Classification of human activity on water through micro-Dopplers using deep convolutional neural networks

Youngwook Kim, Taesup Moon, Taesup Moon, 2016, SPIE Defense + Security.

Prediction Model for Random Variation in FinFET Induced by Line-Edge-Roughness (LER)

Changhwan Shin, Taesup Moon, Jihwan Kwak, 2021, Electronics.

DUDE-Seq: Fast, flexible, and robust denoising of nucleotide sequences

Byunghan Lee, Sungroh Yoon, Tsachy Weissman, 2015 .

DUDE-Seq: Fast Universal Denoising of Nucleotide Sequences pdf

Tsachy Weissman, Byunghan Lee, Sungroh Yoon, 2015, ArXiv.

Uniform Concentration of the Loss Estimator for Neural DUDE pdf

Taesup Moon, Taesup Moon, 2017, ArXiv.

Toward a unified framework for interpreting machine-learning models in neuroimaging

Taesup Moon, Choong-Wan Woo, Sungwoo Lee, 2020, Nature Protocols.

NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations

Taesup Moon, Sungmin Cha, Naeun Ko, 2021, 2106.11644.

Self-Supervised Iterative Contextual Smoothing for Efficient Adversarial Defense against Gray- and Black-Box Attack pdf

Taesup Moon, Sungmin Cha, Naeun Ko, 2021, ArXiv.

DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling pdf

Taesup Moon, Sungmin Cha, Sunghwan Joo, 2019, AAAI.

Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers

Honglak Lee, Moontae Lee, Taesup Moon, 2023, ArXiv.

Fooling Neural Network Interpretations via Adversarial Model Manipulation

Taesup Moon, Juyeon Heo, Sunghwan Joo, 2019, NeurIPS.

Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks

Youngwook Kim, Taesup Moon, Taesup Moon, 2016, IEEE Geoscience and Remote Sensing Letters.

CPR: Classifier-Projection Regularization for Continual Learning pdf

Taesup Moon, Flavio P. Calmon, Sungmin Cha, 2020, ICLR.

SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning pdf

Taesup Moon, Young Joon Yoo, Sungmin Cha, 2021, NeurIPS.

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization

Taesup Moon, Sungmin Cha, Hongjoon Ahn, 2020, NeurIPS.

Uncertainty-based Continual Learning with Adaptive Regularization

Taesup Moon, Hongjoon Ahn, Sungmin Cha, 2019, NeurIPS.

Estimating PM2.5 concentration of the conterminous United States via interpretable convolutional neural networks.

Yang Liu, Yongbee Park, Taesup Moon, 2019, Environmental pollution.

Learning Blind Pixelwise Affine Image Denoiser With Single Noisy Images

Taesup Moon, Jaeseok Byun, Taesup Moon, 2020, IEEE Signal Processing Letters.

Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation

Toan Duc Bui, Jitae Shin, Taesup Moon, 2019, Biomed. Signal Process. Control..

Fair Feature Distillation for Visual Recognition

Taesup Moon, Taeeon Park, Sangwon Jung, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Classification of micro-Doppler signatures of human aquatic activity through simulation and measurement using transferred learning

Youngwook Kim, Taesup Moon, Jinhee Park, 2017, Defense + Security.

Learning to model relatedness for news recommendation

Zhaohui Zheng, Yi Chang, Xuanhui Wang, 2011, WWW.

Subtask Gated Networks for Non-Intrusive Load Monitoring

Wonjong Rhee, Hyoseop Lee, Jaeryun Yim, 2018, AAAI.

An unbiased offline evaluation of contextual bandit algorithms with generalized linear models

Wei Chu, John Langford, Lihong Li, 2011 .

Fully Convolutional Pixel Adaptive Image Denoiser

Taesup Moon, Sungmin Cha, Taesup Moon, 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Discrete Denoising With Shifts

Tsachy Weissman, Taesup Moon, T. Weissman, 2007, IEEE Transactions on Information Theory.

DUDE-Seq: Fast, flexible, and robust denoising of nucleotide sequences

Tsachy Weissman, Byunghan Lee, Sungroh Yoon, 2015 .

GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images

Byeongjoon Kim, Taesup Moon, Jongduk Baek, 2019, ICLR.

Learning Fair Classifiers with Partially Annotated Group Labels

Sanghyuk Chun, Taesup Moon, Sangwon Jung, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Observations on K-Image Expansion of Image-Mixing Augmentation

Jongwon Choi, Sangdoo Yun, Taesup Moon, 2021, IEEE Access.

Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks

Youngwook Kim, Taesup Moon, Jinhee Park, 2016, Sensors.

Sy-CON: Symmetric Contrastive Loss for Continual Self-Supervised Representation Learning

Taesup Moon, Sungmin Cha, 2023, ArXiv.

3D Densely Convolutional Networks for Volumetric Segmentation pdf

Toan Duc Bui, Jitae Shin, Taesup Moon, 2017, ArXiv.

Continual Learning in the Presence of Spurious Correlation

Taesup Moon, Sangwon Jung, Donggyu Lee, 2023, ArXiv.

Towards More Objective Evaluation of Class Incremental Learning: Representation Learning Perspective

Hyunwoo J. Kim, Moontae Lee, Honglak Lee, 2022, 2206.08101.

A Simple Class Decision Balancing for Incremental Learning pdf

Taesup Moon, Hongjoon Ahn, Taesup Moon, 2020, ArXiv.

Adaptive Group Sparse Regularization for Continual Learning pdf

Taesup Moon, Sungmin Cha, Sangwon Jung, 2020, ArXiv.

Issues for Continual Learning in the Presence of Dataset Bias

Taesup Moon, Sangwon Jung, Donggyu Lee, 2023 .

CPR : C LASSIFIER-P ROJECTION R EGULARIZATION FOR C ONTINUAL L EARNING

F. Calmon, Hsiang Hsu, Taesup Moon, 2021 .

Task-Balanced Batch Normalization for Exemplar-based Class-Incremental Learning

Moontae Lee, Taesup Moon, Sungmin Cha, 2022, ArXiv.

RNNDROP: A novel dropout for RNNS in ASR

Inchul Song, Heeyoul Choi, Taesup Moon, 2015, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).

IntervalRank: isotonic regression with listwise and pairwise constraints

Alexander J. Smola, Zhaohui Zheng, Yi Chang, 2010, WSDM '10.

Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks

Rios Jesus Javier, Taesup Moon, Youngwook Kim, 2016, Sensors.

Deep Learning Aided Blind Synchronization Word Estimation

Seok-Ho Chang, Taesup Moon, Sang-Hyo Kim, 2021, IEEE Access.

SwiFT: Swin 4D fMRI Transformer

Jiook Cha, Taesup Moon, Shinjae Yoo, 2023, ArXiv.

Continual Learning of Micro-Doppler Signature-Based Human Activity Classification

Youngwook Kim, Taesup Moon, Donggyu Lee, 2021, IEEE Geoscience and Remote Sensing Letters.

xxAI - Beyond Explainable Artificial Intelligence

Julius von Kügelgen, W. Samek, Amir-Hossein Karimi, 2020, xxAI@ICML.

DUDE-Seq: Fast, flexible, and robust denoising for targeted amplicon sequencing

Sungroh Yoon, T. Weissman, Sungroh Yoon, 2015, PLoS ONE.

Neural Universal Discrete Denoiser

Byunghan Lee, Sungroh Yoon, Taesup Moon, 2016, NIPS.

Rebalancing Batch Normalization for Exemplar-Based Class-Incremental Learning

Moontae Lee, Taesup Moon, Sungmin Cha, 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization

Sanghyuk Chun, Taesup Moon, Taeeon Park, 2023, ICLR.

Regularization and Kernelization of the Maximin Correlation Approach

Sungroh Yoon, Taehoon Lee, Taesup Moon, 2015, IEEE Access.

Universal FIR MMSE Filtering

Tsachy Weissman, Taesup Moon, T. Weissman, 2009, IEEE Transactions on Signal Processing.

Competitive On-line Linear FIR MMSE Filtering

Tsachy Weissman, Taesup Moon, T. Weissman, 2007, 2007 IEEE International Symposium on Information Theory.

Learning from noisy data with applications to filtering and denoising

Taesup Moon, Taesup Moon, 2008 .

An unbiased offline evaluation of contextual bandit algorithms with generalized linear models

J. Langford, Lihong Li, Wei Chu, 2011 .

FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise

Taesup Moon, Sungmin Cha, Jaeseok Byun, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

3D Densely Convolutional Networks for Volumetric Segmentation

Jitae Shin, Taesup Moon, T. Bui, 2017, ArXiv.

SS-IL: Separated Softmax for Incremental Learning

Taesup Moon, Hongjoon Ahn, Jihwan Kwak, 2020, IEEE International Conference on Computer Vision.

Towards Realistic Incremental Scenario in Class Incremental Semantic Segmentation

Taesup Moon, Sungmin Cha, Jihwan Kwak, 2024, arXiv.org.

Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel

Taesup Moon, Taeeon Park, 2020, AISTATS.

Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning

Taesup Moon, Sungmin Cha, Kyunghyun Cho, 2023, 2306.05101.

Towards Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective

Hyunwoo J. Kim, Moontae Lee, Honglak Lee, 2022, 2206.08101.