Publications
-
BAMM: Bidirectional Autoregressive Motion Model
Ekkasit Pinyoanuntapong, Muhammad , Pu Wang, Minwoo Lee, Chen Chen European Conference on Computer Vision (ECCV), 2024
-
Towards Interactive Reinforcement Learning with Intrinsic Feedback
-
MMM: Generative Masked Motion Model
Ekkasit Pinyoanuntapong, Ayman Ali, Pu Wang, Minwoo Lee, Chen Chen IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
-
GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition
Ekkasit Pinyoanuntapong, Ayman Ali, Kalvik Jakkala, Pu Wang, Minwoo Lee, Qucheng Peng, Chen Chen, Zhi Sun Proceedings of e 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS), 2023
-
GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer
Ekkasit Pinyoanuntapong, Aiman Ali, Pu Wang, Minwoo Lee, and Chen Chen.
Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. -
DHA-FL: Enabling Efficient and Effective AIoT via Decentralized Hierarchical Asynchronous Federated Learning
Wesley Houston Huff, pinyarash pinyoanuntapong, Ravikumar Balakrishnan, Hao Feng, Minwoo Lee, Pu Wang, Chen Chen MLSys 2023 Workshop on Resource-Constrained Learning in Wireless Networks, 2023
-
Error-related Potential Variability: Exploring the Effects on Classification and Transferability
Benjamin Poole and Minwoo Lee
IEEE Symposium on Computational Intelligence for Brain Computer Interfaces (CIBCI), Dec. 2022 -
EdgeML: Towards network-accelerated federated learning over wireless edge
Pinyarash Pinyoanuntapong, Prabhu Janakaraj, Ravikumar Balakrishnan, Minwoo Lee, Chen Chen, and Pu Wang
Computer Networks, Vol. 219, Dec. 2022, 109396 -
A Research Plan for Integrating Generative and Cognitive AI for Human Centered, Explainable Co-Creative AI
Maher, M.L., Magerko, B., Ventura, D., Fisher, D., Cardona-Rivera, R., Fulda, N., Gero, J., Lee, M., Wilson, D., Kaufman.
CHI 2022 Workshop on Generative AI and HCI, 2022 (https://generativeaiandhci.github.io/) -
Privacy Enhancement for Cloud-Based Few-Shot Learning
Archit Parnami, Liyue Fan, Minwoo Lee
Proceedings of the 2022 International Joint Conference on Neural Networks, 2022. -
Towards Scalable and Robust AIoT via Decentralized Federated Learning
Pinyarash Pinyoanuntapong, Wesley Houston Huff, Minwoo Lee, Chen Chen, and Pu Wang
IEEE Internet of Things Magazine, 2022 -
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning
Archit Parnami and Minwoo Lee
arXiv preprint arXiv:2203.04291, 2022 -
Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks
Archit Parnami, Mayuri Deshpande, Anant Kumar Mishra, Minwoo Lee
Third International Workshop on Knowledge Graph Construction, 2022 -
Detecting Face-to-Face Interactions with SocialBit: A Novel Algorithm for Wearables,
White K, Reilly D, Scoleri G, Tate S, Mack C, Lee M, Mehl MR, Dhand A, Shin M
Face2face: advancing the science of social interaction, The Royal Society, London, 2022. -
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
Matias Mendieta, Taojiannan Yang, Pu Wang, Minwoo Lee, Zhengming Ding, Chen Chen
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Best Paper Nominee (33 out of 8,161)) -
Few-Shot Keyword Spotting With Prototypical Networks
Archit Parnami and Minwoo Lee
ACM 7th International Conference on Machine Learning Technologies (ICMLT). 2022 -
MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
Taojiannan Yang, Sijie Zhu, Matias Mendieta, Pu Wang, Ravikumar Balakrishnan, Minwoo Lee, Tao Han, Mubarak Shah, Chen Chen
Transactions on Pattern Analysis and Machine Intelligence, 2022 -
Sim-to-Real Transfer in Multi-agent ReinforcementNetworking for Federated Edge Computing
Pinyarash Pinyoanuntapong, Tagore Pothuneedi, Ravikumar Balakrishnan, Minwoo Lee, Chen Chen, and Pu Wang
IEEE SEC/EdgeComm (Invited Paper), 2021 -
Multi-task Transfer with Practice.
Upasana Pattnaik and Minwoo Lee.
IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2021 -
Learning Sparse Evidence-Driven Interpretation to Understand Deep Reinforcement Learning Agents.
Giang Dao, Wesley Huff, and Minwoo Lee.
IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2021 -
Data-driven Safety Risk Prediction of Lithium-ion Battery
Yikai Jia, Jiani Li1, Chunhao Yuan, Xiang Gao, Weiran Yao, Minwoo Lee, Jun Xu Advanced Energy Materials,p2003868, 2021
-
Spatio-Temporal Domain Adaptation for Gait based User Identification from Radar Data
Kalvik Jakkala, Pu Wang, Minwoo Lee, Chen Chen, Arupjyoti Bhuyan, and Zhi Sun, 2020
-
Co-creative Robotic Arm for Differently-Abled Kids: Speech, Sketch Inputs and External Feedbacks for Multiple Drawings.
Sharma Shaikh, Vidhushini Srinivasan, Yue Peng, Minwoo Lee, and Nicholas Davis. Proceedings of the Future Technologies Conference, 2020
-
Demystifying Deep Neural Networks Through Interpretation: A Survey
Giang Dao and Minwoo Lee
arXiv preprint arXiv:2012.07119, 2020 -
FedAir: Towards Multi-hop Federated Learning Over-the-Air
Pinyarash Pinyoanuntapong, Prabhu Janakaraj, Minwoo Lee, Pu Wang, Chen Chen
IEEE SPAWC, 2020 -
Continuous reinforcement learning to adapt multi-objective optimization online for robot motion
Kai Zhang, Sterling McLeod, Minwoo Lee, Jing Xiao
International Journal of Advanced Robotic Systems, 2020 -
Towards In-Band Telemetry for Self Driving Wireless Networks
Prabhu Janakaraj, Pinyarash Pinyoanuntapong, Pu Wang, and Minwoo Lee
2019 IEEE INFOCOM Workshop: NI 2020: Network Intelligence: Machine Learning for Networking, July 2020 -
Efficient Practice for Deep Reinforcement Learning
Venkata Sai Santosh Ravi Teja Kancharla and Minwoo Lee
Proceedings of 2019 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL). Dec 2019 -
Relevant Experiences in Replay Buffer
Giang Dao and Minwoo Lee
Proceedings of 2019 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL). Dec 2019 -
Automatic Composite Action Discovery for Hierarchical Reinforcement Learning
Josiah Laivins and Minwoo Lee
Proceedings of 2019 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL). Dec 2019
-
Distributed Multi-Hop Traffic Engineering via Stochastic Policy Gradient Reinforcement Learning
Pinyarash Pinyoanuntapong, Minwoo Lee, and Pu Wang
IEEE GLOBECOM, Dec 2019 -
STAR: Simultaneous Tracking and Recognition Through Millimeter Waves and Deep Learning
Prabhu Janakaraj, Kalvik Jakkala, Arupjyoti (Arup) Bhuyan, Zhi Sun, Pu Wang, Minwoo Lee
2019 12th IFIP Wireless and Mobile Networking Conference (WMNC), 2019 -
Delay-optimal traffic engineering through multi-agent reinforcement learning
Pinyarash Pinyoanuntapong, Minwoo Lee, and Pu Wang
2019 IEEE INFOCOM Workshop: NI 2019: Network Intelligence: Machine Learning for Networking, Apr 2019 -
Deep CSI Learning for Gait Recognition At-Scale
Kalvik Jakkala, Arupjyoti Bhuyan, Zhi Sun, Pu Wang , and Minwoo Lee, Proceedings of BalkanCom'19, Skopje, Macedonia, June 2019
-
Topological Data Analysis for Discourse Semantics?
Ketki Savle, Wlodek Zadrozny and Minwoo Lee.
The 13th International Conference on Computational Semantics (IWCS'19), May 2019 -
Deep Reinforcement Learning Monitor for Snapshot Recording.
Giang Dao, Indrajeet Mishra, and Minwoo Lee.
The 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'18), December 2018
-
Visual Sparse Bayesian Reinforcement Learning: A Framework for Interpreting What an Agent Has Learned.
Indrajeet Mishra, Giang Dao and Minwoo Lee.
Proceedings of 2018 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2018. -
Unstructured Medical Text Classification Using Linguistic Analysis: A Supervised Deep Learning Approach.
Ahmad Al-Doulat, Islam Obaidat, and Minwoo Lee
15th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2018), October 2018 -
Deep Learning Based Urban Analytics Platform: Applications to Traffic Flow Modeling and Prediction.
Archit Parnami, Prajval Bavi, Dimitris Papanikolaou, Srinivas Akella, Minwoo Lee and Siddharth Krishnan.
ACM SIGKDD Workshop on Mining Urban Data (MUD3), London, UK, 2018 -
Can Reinforcement Learning Agent Practice Before It Starts Learning?
Minwoo Lee and Chuck Anderson.
Proceedings of the 2017 International Joint Conference on Neural Networks, Alaska, USA, 2017. -
Relevance Vector Sampling for Reinforcement Learning in Continuous Action Space.
Minwoo Lee and Chuck Anderson.
The 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'16), December 2016
Related videos are available here. -
Robust Reinforcement Learning with Relevance Vector Machines.
Minwoo Lee and Chuck Anderson.
Robotics: Science and Systems (RSS) 2016 Robot Learning and Planning Workshop, June 2016 -
Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics.
Chuck Anderson, Minwoo Lee, Dan Elliott.
Proceedings of the 2015 International Joint Conference on Neural Networks, Killarney, Ireland, 2015.
(Best Paper Award) -
Convergent Reinforcement Learning Control with Neural Networks and Continuous Action Search.
Minwoo Lee and Chuck Anderson.
Proceedings of 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), pp. 1-8, 2014. -
Automated Cyber-attack Scenario Generation Using the Symbolic Simulation.
Jong-Keun Lee, Min-Woo Lee, Jang-Se Lee, Sung-Do Chi, and Syng-Yup Ohn.
AIS 2004, lNAI 3397, pp. 380-389, 2004 -
DEVS/HLA-Based Modeling and Simulation for Intelligent Transportation Systems.
Jong-Keun Lee, Min-Woo Lee, Sung-Do Chi.
SIMULATION, Vol 79, Issue 8, pp. 423-439, August, 2003.