Publications/International Conferences
2021
◆ Self-supervised pre-training and contrastive representation learning for multiple-choice video QA (Seonhoon Kim, Seohyeong Jeong, Eunbyul Kim, Inho Kang, Nojun Kwak)
◆ IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks (Insu Jeon, Wonkwang Lee, Myeongjang Pyeon, Gunhee Kim)
◆ Texture Generation Using dual-Domain Feature Flow with Multi-View Hallucinations (Seunggyu Chang, Jungchan Cho, Songhwai Oh)
◆ Dual Compositional Learning in Interactive Image Retrieval (Jongseok Kim, Youngjae Yu, Hoeseong Kim, Gunhee Kim)
◆ DramaQA: Character-Centered Video Story Understanding with Hierarchical QA (Seongho Choi, Kyoung-Woon On, Yu-Jung Heo, Ahjeong Seo, Youwon Jang, Minsu Lee, Byoung-Tak Zhang)
◆ Neural Sequence-to-grid Module for Learning Symbolic Rules (Segwang Kim, Hyoungwook Nam, Joonyoung Kim, Kyomin Jung)
◆ Class-Attentive Diffusion Network for Semi-Supervised Classification (Jongin Lim, Daeho Um, Hyung Jin Chang, Dae Ung Jo, Jin Young Choi)
◆ AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks (Youngmin Ro, Jin Young Choi)
◆ Neural Sequence-to-grid Module for Learning Symbolic Rules (Segwang Kim, Hyoungwook Nam, Joonyoung Kim, Kyomin Jung)
2022
◆ Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification (Yinhua Piao, Sangseon Lee, Dohoon Lee, Sun Kim)
◆ Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video (Jinseok Bae, Hojun Jang, Cheol-Hui Min, Hyungun Choi, Young Min Kim)
◆ Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks (Seungyong Moon, Gaon An, Hyun Oh Song)
◆ Texture Generation Using dual-Domain Feature Flow with Multi-View Hallucinations (Seunggyu Chang, Jungchan Cho, Songhwai Oh)
◆ Towards a Rigorous Evaluation of Time-series Anomaly Detection(Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon)
◆ Information-Theoretic Bias Reduction via Causal View of Spurious Correlation(Seonguk Seo, Joon-Young Lee, Bohyung Han)
◆ TrustAL: Trustworthy Active Learning using Knowledge Distillation(Beong-woo Kwak, Youngwook Kim, Yu Jin Kim, Seung-won Hwang, Jinyoung Yeo)
◆ Dual Task Framework for Improving Persona-grounded Dialogue Dataset (Minju Kim, Beong-woo Kwak, Youngwook Kim, Hong-in Lee, Seung-won Hwang, Jinyoung Yeo)
◆ C2L: Causally Contrastive Learning for Robust Text Classification. (Seungtaek Choi, Myeongho Jeong, Hojae Han, Seung-won Hwang)
2023
◆ Long-Term 3D Human Motion Generation from Multiple Action Labels (Taeryung Lee, Gyeongsik Moon, Kyoung Mu Lee)
◆ Unifying Vision-Language Representation Space with Single-tower Transformer (Jiho Jang, Chaerin Kong, Donghyeon Jeon, Seonhoon Kim, Nojun Kwak)
◆ Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion (Jungwook Shin, Jaeill Kim, Kyungeun Lee, Hyunghun Cho, Wonjong Rhee)
◆ Towards More Robust Interpretation via Local Gradient Alignment (Sunghwan Joo, SeokHyeon Jeong, Juyeon Heo, Adrian Weller, and Taesup Moon)
◆ Script, Language, and Labels: Overcoming Three Discrepancies for Low-Resource Language Specialization (Jaeseong Lee, Dohyeon Lee, Seung-won Hwang)
◆ Prompt-Augmented Linear Probing: Scaling Beyond The Limit of Few-shot In-Context Learners (Hyunsoo Cho, Hyuhng Joon Kim, Junyeob Kim, Sang-Woo Lee, Sang-goo Lee, Kang Min Yoo, Taeuk Kim)
◆ Neural Diffeomorphic Non-Uniform B-Spline Flows (S M Hong, S Y Chun)
◆ Self-supervised pre-training and contrastive representation learning for multiple-choice video QA (Seonhoon Kim, Seohyeong Jeong, Eunbyul Kim, Inho Kang, Nojun Kwak)
◆ IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks (Insu Jeon, Wonkwang Lee, Myeongjang Pyeon, Gunhee Kim)
◆ Texture Generation Using dual-Domain Feature Flow with Multi-View Hallucinations (Seunggyu Chang, Jungchan Cho, Songhwai Oh)
◆ Dual Compositional Learning in Interactive Image Retrieval (Jongseok Kim, Youngjae Yu, Hoeseong Kim, Gunhee Kim)
◆ DramaQA: Character-Centered Video Story Understanding with Hierarchical QA (Seongho Choi, Kyoung-Woon On, Yu-Jung Heo, Ahjeong Seo, Youwon Jang, Minsu Lee, Byoung-Tak Zhang)
◆ Neural Sequence-to-grid Module for Learning Symbolic Rules (Segwang Kim, Hyoungwook Nam, Joonyoung Kim, Kyomin Jung)
◆ Class-Attentive Diffusion Network for Semi-Supervised Classification (Jongin Lim, Daeho Um, Hyung Jin Chang, Dae Ung Jo, Jin Young Choi)
◆ AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks (Youngmin Ro, Jin Young Choi)
◆ Neural Sequence-to-grid Module for Learning Symbolic Rules (Segwang Kim, Hyoungwook Nam, Joonyoung Kim, Kyomin Jung)
2022
◆ Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification (Yinhua Piao, Sangseon Lee, Dohoon Lee, Sun Kim)
◆ Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video (Jinseok Bae, Hojun Jang, Cheol-Hui Min, Hyungun Choi, Young Min Kim)
◆ Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks (Seungyong Moon, Gaon An, Hyun Oh Song)
◆ Texture Generation Using dual-Domain Feature Flow with Multi-View Hallucinations (Seunggyu Chang, Jungchan Cho, Songhwai Oh)
◆ Towards a Rigorous Evaluation of Time-series Anomaly Detection(Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon)
◆ Information-Theoretic Bias Reduction via Causal View of Spurious Correlation(Seonguk Seo, Joon-Young Lee, Bohyung Han)
◆ TrustAL: Trustworthy Active Learning using Knowledge Distillation(Beong-woo Kwak, Youngwook Kim, Yu Jin Kim, Seung-won Hwang, Jinyoung Yeo)
◆ Dual Task Framework for Improving Persona-grounded Dialogue Dataset (Minju Kim, Beong-woo Kwak, Youngwook Kim, Hong-in Lee, Seung-won Hwang, Jinyoung Yeo)
◆ C2L: Causally Contrastive Learning for Robust Text Classification. (Seungtaek Choi, Myeongho Jeong, Hojae Han, Seung-won Hwang)
2023
◆ Long-Term 3D Human Motion Generation from Multiple Action Labels (Taeryung Lee, Gyeongsik Moon, Kyoung Mu Lee)
◆ Unifying Vision-Language Representation Space with Single-tower Transformer (Jiho Jang, Chaerin Kong, Donghyeon Jeon, Seonhoon Kim, Nojun Kwak)
◆ Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion (Jungwook Shin, Jaeill Kim, Kyungeun Lee, Hyunghun Cho, Wonjong Rhee)
◆ Towards More Robust Interpretation via Local Gradient Alignment (Sunghwan Joo, SeokHyeon Jeong, Juyeon Heo, Adrian Weller, and Taesup Moon)
◆ Script, Language, and Labels: Overcoming Three Discrepancies for Low-Resource Language Specialization (Jaeseong Lee, Dohyeon Lee, Seung-won Hwang)
◆ Prompt-Augmented Linear Probing: Scaling Beyond The Limit of Few-shot In-Context Learners (Hyunsoo Cho, Hyuhng Joon Kim, Junyeob Kim, Sang-Woo Lee, Sang-goo Lee, Kang Min Yoo, Taeuk Kim)
◆ Neural Diffeomorphic Non-Uniform B-Spline Flows (S M Hong, S Y Chun)
2021
◆ Self-Supervised Learning of Compressed Video Representations (Youngjae Yu, Sangho Lee, Gunhee Kim, Yale Song)
◆ SEDONA: Search for Decoupled Neural Networks Toward Greedy Block-wise Learning (Myeongjang Pyeon, Jihwan Moon, Taeyoung Hahn, Gunhee Kim)
◆ Parameter Efficient Multimodal Transformers for Video Representation Learning (Sangho Lee, Youngjae Yu, Gunhee Kim, Thomas Breuel, Jan Kautz, Yale Song)
◆ Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration (Jaekyeom Kim, Minjung Kim, Dongyeon Woo & Gunhee Kim )
◆ CPR: CLASSIFIER-PROJECTION REGULARIZATION FOR CONTINUAL LEARNING (Sungmin Cha, Hsiang Hsu, Taebaek Hwang, Flavio P. Calmon, Taesup Moon)
◆ GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images (Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon)
◆ Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity (Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song)
◆ Removing Undesirable Feature Contributions Using Out-of-Distribution Data (Saehyung Lee, Changhwa Park, Hyungyu Lee, Jihun Yi, Jonghyun Lee, Sungroh Yoon)
◆ Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech(Yoonhyung Lee, Joongbo Shin, Kyomin Jung)
2022
◆ Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs (Jaewoong Choi, Junho Lee, Changyeon Yoon, Jung Ho Park, Geonho Hwang, Myungjoo Kang)
◆ Lipschitz-constrained Unsupervised Skill Discovery (Seohong Park, Jongwook Choi, Jaekyeom Kim, Honglak Lee, Gunhee Kim)l
◆ Neural Variational Dropout Processes (Insu Jeon, Youngjin Park, Gunhee Kim)
◆ Probabilistic Implicit Scene Completion (Dongsu Zhang, Changwoon Choi, Inbum Park, Young Min Kim)
◆ PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior (Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu)
◆ Stein Latent Optimization for Generative Adversarial Networks(Uiwon Hwang, Heeseung Kim, Dahuin Jung, Hyemi Jang, Hyungyu Lee, Sungroh Yoon)
◆ Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring (Seungjun Nah, Sanghyun Son, Jaerin Lee, Kyoung Mu Lee)
◆ SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search (Hyeonmin Ha, Ji-Hoon Kim, Semin Park, Byung-Gon Chun)
2023
◆ Confidence-Based Feature Imputation for Graphs with Partially Known Features (Daeho Um, Jiwoong Park, Seulki Park, Jin young Choi)
◆ Exact Group Fairness Regularization via Classwise Robust Optimization (Sangwon Jung, Taeeon Park, Sanghyuk Chun, Taesup Moon)
◆ New Insights for the Stability-Plasticity Dilemma in Online Continual Learning (Dahuin Jung, Dongjin Lee, Sunwon Hong, Hyemi Jang, Ho Bae, Sungroh Yoon)
◆ BigVGAN: A Universal Neural Vocoder with Large-Scale Training (Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon)
◆ Empirical Study of Pre-training a Backbone for 3D Human Pose and Shape Estimation (Hongsuk Choi, Hyeongjin Nam, Taeryung Lee, Gyeongsik Moon, Kyoung Mu Lee)
◆ NERDS: A General Framework to Train Camera Denoisers from Raw-RGB Noisy Image Pairs (Heewon Kim, Kyoung Mu Lee)
◆ Learning without Prejudices: Continual Unbiased Learning via Benign and Malignant Forgetting (Myeongho Jeon, Hyoje Lee, Yedarm Seong, Myungjoo Kang)
◆ Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic (Yulhwa Kim ~Yulhwa_Kim1 , Jaeyong Jang, Jehun Lee, Jihoon Park, Jeonghoon Kim, Byeongwook Kim, Baeseong park, Se Jung Kwon, Dongsoo Lee, jae-joon kim)
◆ Outcome-Directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal Generation (Daesol Cho, Seungjae Lee, and H. Jin Kim)
◆ Geometrically Regularized Autoencoders for Non-Euclidean Data (Seungyeon Kim, Taegyun Ahn, Yonghyeon Lee, Jihwan Kim, Michael Yu Wang, and Frank C. Park)
◆ Self-Supervised Learning of Compressed Video Representations (Youngjae Yu, Sangho Lee, Gunhee Kim, Yale Song)
◆ SEDONA: Search for Decoupled Neural Networks Toward Greedy Block-wise Learning (Myeongjang Pyeon, Jihwan Moon, Taeyoung Hahn, Gunhee Kim)
◆ Parameter Efficient Multimodal Transformers for Video Representation Learning (Sangho Lee, Youngjae Yu, Gunhee Kim, Thomas Breuel, Jan Kautz, Yale Song)
◆ Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration (Jaekyeom Kim, Minjung Kim, Dongyeon Woo & Gunhee Kim )
◆ CPR: CLASSIFIER-PROJECTION REGULARIZATION FOR CONTINUAL LEARNING (Sungmin Cha, Hsiang Hsu, Taebaek Hwang, Flavio P. Calmon, Taesup Moon)
◆ GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images (Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon)
◆ Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity (Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song)
◆ Removing Undesirable Feature Contributions Using Out-of-Distribution Data (Saehyung Lee, Changhwa Park, Hyungyu Lee, Jihun Yi, Jonghyun Lee, Sungroh Yoon)
◆ Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech(Yoonhyung Lee, Joongbo Shin, Kyomin Jung)
2022
◆ Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs (Jaewoong Choi, Junho Lee, Changyeon Yoon, Jung Ho Park, Geonho Hwang, Myungjoo Kang)
◆ Lipschitz-constrained Unsupervised Skill Discovery (Seohong Park, Jongwook Choi, Jaekyeom Kim, Honglak Lee, Gunhee Kim)l
◆ Neural Variational Dropout Processes (Insu Jeon, Youngjin Park, Gunhee Kim)
◆ Probabilistic Implicit Scene Completion (Dongsu Zhang, Changwoon Choi, Inbum Park, Young Min Kim)
◆ PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior (Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu)
◆ Stein Latent Optimization for Generative Adversarial Networks(Uiwon Hwang, Heeseung Kim, Dahuin Jung, Hyemi Jang, Hyungyu Lee, Sungroh Yoon)
◆ Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring (Seungjun Nah, Sanghyun Son, Jaerin Lee, Kyoung Mu Lee)
◆ SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search (Hyeonmin Ha, Ji-Hoon Kim, Semin Park, Byung-Gon Chun)
2023
◆ Confidence-Based Feature Imputation for Graphs with Partially Known Features (Daeho Um, Jiwoong Park, Seulki Park, Jin young Choi)
◆ Exact Group Fairness Regularization via Classwise Robust Optimization (Sangwon Jung, Taeeon Park, Sanghyuk Chun, Taesup Moon)
◆ New Insights for the Stability-Plasticity Dilemma in Online Continual Learning (Dahuin Jung, Dongjin Lee, Sunwon Hong, Hyemi Jang, Ho Bae, Sungroh Yoon)
◆ BigVGAN: A Universal Neural Vocoder with Large-Scale Training (Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon)
◆ Empirical Study of Pre-training a Backbone for 3D Human Pose and Shape Estimation (Hongsuk Choi, Hyeongjin Nam, Taeryung Lee, Gyeongsik Moon, Kyoung Mu Lee)
◆ NERDS: A General Framework to Train Camera Denoisers from Raw-RGB Noisy Image Pairs (Heewon Kim, Kyoung Mu Lee)
◆ Learning without Prejudices: Continual Unbiased Learning via Benign and Malignant Forgetting (Myeongho Jeon, Hyoje Lee, Yedarm Seong, Myungjoo Kang)
◆ Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic (Yulhwa Kim ~Yulhwa_Kim1 , Jaeyong Jang, Jehun Lee, Jihoon Park, Jeonghoon Kim, Byeongwook Kim, Baeseong park, Se Jung Kwon, Dongsoo Lee, jae-joon kim)
◆ Outcome-Directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal Generation (Daesol Cho, Seungjae Lee, and H. Jin Kim)
◆ Geometrically Regularized Autoencoders for Non-Euclidean Data (Seungyeon Kim, Taegyun Ahn, Yonghyeon Lee, Jihwan Kim, Michael Yu Wang, and Frank C. Park)
2021
◆ Unsupervised Skill Discovery with Bottleneck Option Learning (Jaekyeom Kim, Seohong Park, Gunhee Kim)
◆ Unsupervised Representation Learning via Neural Activation Coding (Yookoon Park, Sangho Lee, Gunhee Kim, David M. Blei)
◆ Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning (Taehyeong Kim, Injune Hwang, Hyundo Lee, Hyunseo Kim, Won-Seok Choi, Joseph J. Lim, Byoung-Tak Zhang)
2022
◆ Variational On-the-Fly Personalization (Jangho Kim, Jun-Tae Lee, Simyung Chang, Nojun Kwak)
◆ Dataset Condensation via Efficient Synthetic-Data Parameterization (Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song)
◆ Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization (Deokjae Lee, Seungyong Moon, Junhyeok Lee, Hyun Oh Song)
◆ AutoSNN: Towards Energy-Efficient Spiking Neural Networks (Byunggook Na, Jisoo Mok, Seongsik Park, Dongjin Lee, Hyeokjun Choe, Sungroh Yoon)
◆ Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance (Heeseung Kim, Sungwon Kim, Sungroh Yoon)
◆ Confidence Score for Source-Free Unsupervised Domain Adaptation (Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon)
◆ Dataset Condensation with Contrastive Signals (Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon)
◆ Multi-Level Branched Regularization for Federated Learning (Jinkyu Kim, Geeho Kim, Bohyung Han)
◆ Unsupervised Skill Discovery with Bottleneck Option Learning (Jaekyeom Kim, Seohong Park, Gunhee Kim)
◆ Unsupervised Representation Learning via Neural Activation Coding (Yookoon Park, Sangho Lee, Gunhee Kim, David M. Blei)
◆ Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning (Taehyeong Kim, Injune Hwang, Hyundo Lee, Hyunseo Kim, Won-Seok Choi, Joseph J. Lim, Byoung-Tak Zhang)
2022
◆ Variational On-the-Fly Personalization (Jangho Kim, Jun-Tae Lee, Simyung Chang, Nojun Kwak)
◆ Dataset Condensation via Efficient Synthetic-Data Parameterization (Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song)
◆ Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization (Deokjae Lee, Seungyong Moon, Junhyeok Lee, Hyun Oh Song)
◆ AutoSNN: Towards Energy-Efficient Spiking Neural Networks (Byunggook Na, Jisoo Mok, Seongsik Park, Dongjin Lee, Hyeokjun Choe, Sungroh Yoon)
◆ Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance (Heeseung Kim, Sungwon Kim, Sungroh Yoon)
◆ Confidence Score for Source-Free Unsupervised Domain Adaptation (Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon)
◆ Dataset Condensation with Contrastive Signals (Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon)
◆ Multi-Level Branched Regularization for Federated Learning (Jinkyu Kim, Geeho Kim, Bohyung Han)
2021
◆ Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods (Seohong Park, Jaekyeom Kim, Gunhee Kim)
◆ Constrained GPI for Zero-Shot Transfer in Reinforcement Learning (Jaekyeom Kim, Seohong Park, Gunhee Kim)
◆ SSUL: Semantic Segmentation with Unknown Label for Exemplar based Class-Incremental Learning (Sungmin Cha, Beomyoung Kim, Youngjoon Yoo, Taesup Moon)
◆ Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks (Hongjoon Ahn, Yongyi Yang, Quan Gan, Taesup Moon, David Wipf)
◆ Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble (Gaon An, Seungyong Moon, Jang-Hyun Kim, Hyun Oh Song)
◆ Rethinking Value Function Learning for Generalization in Reinforcement Learning (Seungyong Moon, JunYeong Lee, Hyun Oh Song)
◆ Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation(Jungbeom Lee, Jooyoung Choi, Jisoo Mok, Sungroh Yoon)
◆ Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations(Hyeong-Seok Choi, Juheon Lee, Wansoo Kim, Jie Hwan Lee, Hoon Heo, Kyogu Lee)
◆ Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models(Taeuk Kim, Bowen Li, Sang-goo Lee)
◆ Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning(Kibeom Kim, Min Whoo Lee, Yoonsung Kim, Je-Hwan Ryu, Minsu Lee, Byoung-Tak Zhang)
2022
◆ Robust Imitation via Mirror Descent Inverse Reinforcement Learning (Dong-Sig Han, Hyunseo Kim, Hyundo Lee, Je-Hwan Ryu, Byoung-Tak Zhang)
◆ SelecMix: Debiased Learning by Contradicting-pair Sampling(Inwoo Hwang, Sangjun Lee, Yunhyeok Kwak, Seong Joon Oh, Damien Teney, Jin-Hwa Kim, Byoung-Tak Zhang)
◆ Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs (Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeongin Yu, Byung-Gon Chun)
◆ Learning Student-Friendly Teacher Networks for Knowledge Distillation (Dae Young Park, Moon-Hyun Cha, Changwook Jeong, Dae Sin Kim, Bohyung Han)
◆ Learning Debiased and Disentangled Representations for Semantic Segmentation (Sanghyeok Chu, Dongwan Kim, Bohyung Han)
◆ Learning Student-Friendly Teacher Networks for Knowledge Distillation (Dae Young Park, Moon-Hyun Cha, Changwook Jeong, Dae Sin Kim, Bohyung Han)
◆ MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators (Jinyoung Choi, Bohyung Han)
◆ Locally Hierarchical Auto-Regressive Modeling for Image Generation (Tackgeun You, Saehoon Kim, Chiheon Kim, Doyup Lee, Bohyung Han)
◆ Information-Theoretic GAN Compression with Variational Energy-based Model (Minsoo Kang, Hyewon Yoo, Eunhee Kang, Sehwan Ki, Hyong-Euk Lee, Bohyung Han)
◆ S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning (Daesol Cho, Dongseok Shim, H. Jin Kim)
◆ DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning (Seungjae Lee, Jigang Kim, Inkyu Jang, H. Jin Kim)
◆ Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods (Seohong Park, Jaekyeom Kim, Gunhee Kim)
◆ Constrained GPI for Zero-Shot Transfer in Reinforcement Learning (Jaekyeom Kim, Seohong Park, Gunhee Kim)
◆ SSUL: Semantic Segmentation with Unknown Label for Exemplar based Class-Incremental Learning (Sungmin Cha, Beomyoung Kim, Youngjoon Yoo, Taesup Moon)
◆ Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks (Hongjoon Ahn, Yongyi Yang, Quan Gan, Taesup Moon, David Wipf)
◆ Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble (Gaon An, Seungyong Moon, Jang-Hyun Kim, Hyun Oh Song)
◆ Rethinking Value Function Learning for Generalization in Reinforcement Learning (Seungyong Moon, JunYeong Lee, Hyun Oh Song)
◆ Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation(Jungbeom Lee, Jooyoung Choi, Jisoo Mok, Sungroh Yoon)
◆ Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations(Hyeong-Seok Choi, Juheon Lee, Wansoo Kim, Jie Hwan Lee, Hoon Heo, Kyogu Lee)
◆ Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models(Taeuk Kim, Bowen Li, Sang-goo Lee)
◆ Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning(Kibeom Kim, Min Whoo Lee, Yoonsung Kim, Je-Hwan Ryu, Minsu Lee, Byoung-Tak Zhang)
2022
◆ Robust Imitation via Mirror Descent Inverse Reinforcement Learning (Dong-Sig Han, Hyunseo Kim, Hyundo Lee, Je-Hwan Ryu, Byoung-Tak Zhang)
◆ SelecMix: Debiased Learning by Contradicting-pair Sampling(Inwoo Hwang, Sangjun Lee, Yunhyeok Kwak, Seong Joon Oh, Damien Teney, Jin-Hwa Kim, Byoung-Tak Zhang)
◆ Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs (Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeongin Yu, Byung-Gon Chun)
◆ Learning Student-Friendly Teacher Networks for Knowledge Distillation (Dae Young Park, Moon-Hyun Cha, Changwook Jeong, Dae Sin Kim, Bohyung Han)
◆ Learning Debiased and Disentangled Representations for Semantic Segmentation (Sanghyeok Chu, Dongwan Kim, Bohyung Han)
◆ Learning Student-Friendly Teacher Networks for Knowledge Distillation (Dae Young Park, Moon-Hyun Cha, Changwook Jeong, Dae Sin Kim, Bohyung Han)
◆ MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators (Jinyoung Choi, Bohyung Han)
◆ Locally Hierarchical Auto-Regressive Modeling for Image Generation (Tackgeun You, Saehoon Kim, Chiheon Kim, Doyup Lee, Bohyung Han)
◆ Information-Theoretic GAN Compression with Variational Energy-based Model (Minsoo Kang, Hyewon Yoo, Eunhee Kang, Sehwan Ki, Hyong-Euk Lee, Bohyung Han)
◆ S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning (Daesol Cho, Dongseok Shim, H. Jin Kim)
◆ DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning (Seungjae Lee, Jigang Kim, Inkyu Jang, H. Jin Kim)
2021
◆ Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods (Seohong Park, Jaekyeom Kim, Gunhee Kim)
◆ Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods (Seohong Park, Jaekyeom Kim, Gunhee Kim)
to be updated
to be updated
2021
◆Self-Guided Contrastive Learning for BERT Sentence Representations (Taeuk Kim, Kang Min Yoo, Sang-goo Lee)
2022
◆ Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation (KiYoon Yoo, Jangho Kim, Jiho Jang, and Nojun Kwak)
◆ Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings (Sangwon Yu, Jongyoon Song, Heeseung Kim, Seong-min Lee, Woo-Jong Ryu, Sungroh Yoon)
◆ Hypergraph Transformer: Weakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering (Yu-Jung Heo, Eun-Sol Kim, Woo Suk Choi, and Byoung-Tak Zhang)
◆ Plug-and-Play Adaptation for Continuously-updated QA (Kyungjae Lee, Wookje Han, Seung-won Hwang, Hwaran Lee, Joonsuk Park, and Sang-Woo Lee)
◆ Debiasing Event Understanding for Visual Commonsense Tasks (Minji Seo, YeonJoon Jung, Seungtaek Choi, Seung-won Hwang, and Bei Liu)
◆ ReACC: A Retrieval-Augmented Code Completion Framework (Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, Alexey Svyatkovskiy)
2023
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Publication/International Journals
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