Papers
There are 108 references in this bibliography folder.
Jinhyeon Kim, Donghoon Ham, Jeong-Gwan Lee, and Kee-Eung Kim:
End-to-End Document-Grounded Conversation with Encoder-Decoder Pre-Trained Language Model
In: AAAI Conference on Artificial Intelligence (AAAI) DSTC9 Workshop.
2021.
Deunsol Yoon*, Sunghoon Hong*, Byung-Jun Lee, and Kee-Eung Kim:
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic
In: International Conference on Learning Representations (ICLR).
2021.
Youngsoo Jang, Seokin Seo, Jongmin Lee, and Kee-Eung Kim:
Monte-Carlo Planning and Learning with Language Action Value Estimates
In: International Conference on Learning Representations (ICLR).
2021.
Byung-Jun Lee, Jongmin Lee, and Kee-Eung Kim:
Representation Balancing Offline Model-based Reinforcement Learning
In: International Conference on Learning Representations (ICLR).
2021.
이병준, 이종민, 최윤선, 장영수, and 김기응:
효율적인 평생학습 알고리즘의 모델기반 강화학습 적용에 관한 연구
In: 한국소프트웨어종합학술대회, 한국정보과학회.
2020.
HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, and Kee-Eung Kim:
Variational Interaction Information Maximization for Cross-domain Disentanglement
In: Proceedings of Neural Information Processing Systems (NeurIPS).
2020.
Jongmin Lee, Byung-Jun Lee, and Kee-Eung Kim:
Reinforcement Learning for Control with Multiple Frequencies
In: Proceedings of Neural Information Processing Systems (NeurIPS).
2020.
Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang, and Kee-Eung Kim:
Variational Inference for Sequential Data with Future Likelihood Estimates
In: Proceedings of the International Conference on Machine Learning (ICML).
2020.
Byung-Jun Lee*, Jongmin Lee*, Peter Vrancx, Dongho Kim, and Kee-Eung Kim:
Batch Reinforcement Learning with Hyperparameter Gradients
In: Proceedings of the International Conference on Machine Learning (ICML).
2020.
Donghoon Ham*, Jeong-Gwan Lee*, Youngsoo Jang, and Kee-Eung Kim:
End-to-End Neural Pipeline for Goal-Oriented Dialogue System using GPT-2
In: Annual Conference of the Association for Computational Linguistics (ACL).
2020.
Donghoon Ham*, Jeong-Gwan Lee*, Youngsoo Jang, and Kee-Eung Kim:
End-to-End Neural Pipeline for Goal-Oriented Dialogue System using GPT-2
In: AAAI Conference on Artificial Intelligence (AAAI) DSTC8 Workshop.
2020.
Byung-Jun Lee, Seunghoon Hong, and Kee-Eung Kim:
Residual Neural Processes
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
2020.
Youngsoo Jang, Jongmin Lee, and Kee-Eung Kim:
Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
2020.
Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim, and Kee-Eung Kim:
Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
2020.
김건형, 장영수, 이종민, and 김기응:
몬테 카를로 목표를 위한 분산 감소 방법
한국통신학회 하계종합학술발표회.
2019.
이종민, 김건형, and 김기응:
연속 행동공간에서의 몬테-카를로 트리 탐색에 관한 연구
한국통신학회 하계종합학술발표회.
2019.
Nianyin Zeng, Zidong Wang, Hong Zhang, Kee-Eung Kim, Yurong Li, and Xiaohui Liu:
An Improved Particle Filter With a Novel Hybrid Proposal Distribution for Quantitative Analysis of Gold Immunochromatographic Strips
IEEE Transactions on Nanotechnology, 18:819-829.
2019.
Yung-Kyun Noh, Ji Young Park, Byoung Geol Choi, Kee-Eung Kim, and Seung-Woon Rha:
A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization
Journal of Medical Systems.
2019.
Youngsoo Jang, Jongmin Lee, and Kee-Eung Kim:
Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues
In: Neural Information Processing Systems (NeurIPS) Conversational AI workshop.
2019.
Geon-Hyeong Kim, Youngsoo Jang, Jongmin Lee, Wonseok Jeon, Hongseok Yang, and Kee-Eung Kim:
Trust Region Sequential Variational Inference
In: Proceedings of Asian Conference on Machine Learning (ACML).
2019.
Youngsoo Jang*, Jongmin Lee*, Jaeyoung Park*, Kyeng-Hun Lee, Pierre Lison, and Kee-Eung Kim:
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP) System Demonstrations.
2019.
강민구 and 김기응:
강화학습을 이용한 초고속비행체 제어기 학습
In: 한국군사과학기술학회 종합학술대회.
2019.
Kanghoon Lee, Geon-Hyeong Kim, Pedro Ortega, Daniel D. Lee, and Kee-Eung Kim:
Bayesian optimistic Kullback–Leibler exploration
Machine Learning Journal (MLJ), 108.
2019.
김건형, 장영수, 이종민, and 김기응:
모델 기반 베이지안 강화학습의 연속된 도메인으로의 확장에 대한 연구
한국통신학회 하계종합학술발표회 논문집.
2018.
Wonseok Jeon, Seokin Seo, and Kee-Eung Kim:
A Bayesian Approach to Generative Adversarial Imitation Learning
In: Proceedings of Neural Information Processing Systems (NeurIPS).
2018.
Document Actions