Publications


Journal Papers

  • P. Bertens, S. -W Lee.,
    Network of evolvable neural units can learn synaptic learning rules and spiking dynamics,
    Nature Machine Intelligence. (2020) (Cover Paper)
  • D. J. Kim., G. Y. Park., John P. O., S. W Lee.,
    Task complexity interacts with state-space uncertainty in the arbitration between model-based and model-free learning,
    Nature Communications. (Dec 2019)
  • H-G Jung., S. -W. Lee.,
    Few-Shot Learning with Geometric Constraints,
    IEEE Transactions on Neural Networks and Learning Systems (2020)
  • Zhang, S., Yoshida, W., Mano, H., Yanagisawa, T., Mancini, F., Shibata, K., et al.
    Pain control by co-adaptive learning in a brain-machine interface,
    Current Biology, 1–18.(2020)
  • Yanagisawa, T., Fukuma, R., Seymour, B., Tanaka, M., Hosomi, K., Yamashita, O., et al.
    BCI training to move a virtual hand reduces phantom limb pain,
    Neurology, 95(4), e417–e426.(2020)
  • Seymour, B., Mancini, F.
    Hierarchical models of pain: Inference, information-seeking, and adaptive control.,
     NeuroImage, 222, 117212.(2020)
  • Becker, S., Löffler, M., Seymour, B.
    Reward enhances pain discrimination in humans.
    Psychological Science, 31(9), 1191–1199. (2020)
  • E. W. Kim., M. S. Lee., S. H. Oh.,
    Nonconvex Sparse Representation with Slowly Vanishing Gradient Regularizers,
    IEEE Access, vol. 8, pp. 132489-132501 (2020).
  • F. Ke., S. Choi., Y. H. Kang., K. A. Cheon., S. W. Lee
    Exploring the Structural and Strategic Bases of Autism Spectrum Disorders with Deep Learning,
    IEEE Access, vol. 8, pp. 153341-153352 (2020).
  • M. R. Song., S. W. Lee.,
    Dynamic resource allocation during reinforcement learning accounts for ramping and phasic dopamine activity,
    Neural Networks, vol. 126, pp. 95-107 (2020).
  • Marilina Mastrogiuseppe, Natasha Bertelsen, Maria Francesca Bedeschi, S. A. Lee.
    The spatiotemporal organization of episodic memory and its disruption in a neurodevelopmental disorder,
    Scientific Reports 9.1 (2019): 1-12. (Dec 2019)
  • E. W. Kim., M. S. Lee., S. H. Oh.,
    A Scalable Framework for Data-Driven Subspace Representation and Clustering,
    Pattern Recognition Letters, vol. 125, pp. 742-749, (Jul. 2019).

International Conference

  • W-J. Nam,, J. Choi,, S. -W. Lee,

    Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations,

    AAAI Conference on Artificial Intelligence (AAAI) (2021) (Accepted).

  • S.-H. Lee, H.-W. Yoon, H.-R. Noh, J.-H. Kim, and S.-W. Lee,

    Multi-SpectroGAN: High-Diversity and High-Fidelity Spectrogram Generation with Adversarial Style Recombination for Speech Synthesis,

    AAAI Conference on Artificial Intelligence (AAAI) (2021) (Accepted).

  • Y-J. Cha, S. W. Lee,

    Human Uncertainty Inference via Deterministic Ensemble Neural Networks,

    AAAI Conference on Artificial Intelligence (AAAI) (2021) (Accepted).

  • G. Park, J. Y. Yang., S. J. H., E. Yang.

    Attribution Preservation in Network Compression for Reliable Network Interpretation,

    Conference on Neural Information Processing Systems (NeurIPS) (2020).

  • W-J. Nam, S. Gur, J. Choi, L. Wolf, S. -W. Lee.,

    Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks,

    AAAI Conference on Artificial Intelligence (AAAI) (2020).

  • I. Chung., S. Kim., J. Lee., K. J. Kim., S. J. Hwang., E. Yang.

    Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare,

    AAAI Conference on Artificial Intelligence (AAAI) (2020).

  • J. Yi., J. Lee., K. J. Kim., S. J. Hwang., E. Yang.

    Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks,

    International Conference on Learning Representations (ICLR) (2020).

  • Y. Choi., H. Kee., K. Lee., J. Choy., J. Min., S. Lee., S. H. Oh.

    Hierarchical 6-DoF Grasping with Approaching Direction Selection,

    IEEE International Conference on Robotics and Automation (ICRA) (2020).

  • G. Lee., S. -W. Lee.

    Uncertainty-Aware Mesh Decoder for High Fidelity 3D Face Reconstruction,

    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020).

  • J. H. Yun., Peng Zheng, E. H. Yang., Aurelie C. Lozano, Aleksandr Aravkin,
    Trimming the L1 Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning,
    International Conference on Machine Learning (ICML) (2019). (Oral Presentation)

  • C. H. Ahn., E. W. Kim., S. H. Oh.,
    Deep Elastic Networks with Model Selection for Multi-Task Learning,
    International Conference on Computer Vision (ICCV) (2019).

  • G. H. Cha., M. S. Lee., S. H. Oh.,
    Unsupervised 3D Reconstruction Networks,
    International Conference on Computer Vision (ICCV) (2019).

  • N. Newcombe., A. Duval., S. A. Lee., A. Shusterman., N. Miller.

    Getting Our Bearings: Advances in Understanding Spatial Reorientation (Mapping Spatial Geometry: The Role of Vision),

    Annual Meeting of the Cognitive Science Society (CogSci) (2020).

  • Y. J. Rah., S. A. Lee.

    Effects of spatial boundary on episodic memory in children,

    Flux Congress for developmental cognitive neuroscience (2020).

  • G. Kim., H. -G. Jung., S. -W. Lee.

    Few-Shot Object Detection via Knowledge Transfer,

    IEEE International Conference on Systems, Man, and Cybernetics (2020).

  • J. Ryu., S. W. Lee.

    Brain-Like Autoencoder That Learns Latent Covariance Structure,

    From Neuroscience to Artificially Intelligent Systems (NAISys) (2020).

  • J. H. Shin., J. H. Lee., S. W. Lee.

    Deep Interaction between Reinforcement Learning Algorithms and Human Reinforcement Learning,

    From Neuroscience to Artificially Intelligent Systems (NAISys) (2020).

  • J. Park., K. Han., Y. Jeong., S. W. Lee.

    Phonemic-level duration control using attention alignment for natural speech synthesis,

    International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2020). (Oral Presentation)

  • S. Jung., J. Park., S. W. Lee.

    Polyphonic sound event detection using convolutional bidirectional LSTM and synthetic data-based transfer learning,

    International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2020).

  • M. R. Song., S. W. Lee.,
    Dynamic resource allocation during reinforcement learning accounts for ramping and phasic dopamine activity,
    Computational and Systems Neuroscience (COSYNE) (2020).
  • J. Y. Kim., Y.  J. Rah., S. A. Lee.,
    The role of spatial boundaries in episodic memory in young children,
    International Brain Research Organization (2019).
  • J. E. Hwang., J. H. Park., S. A. Lee.,

    Temporal Order Memory Performance as a Behavioral Biomarker of Alzheimer’s Disease,

    International Brain Research Organization (2019).

  • Y. J. Rah., J. H. Shin., S. A. Lee.,
    Dissociable neural signatures of prefrontal cortex for subjective and objective memory performance,
    International Brain Research Organization (2019).

  • M. J. Kang., K. J. Lee., S. H. Oh.,
    Soft Action Particle Deep Reinforcement Learning for a Continuous Action Space,
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019).

  • Namrata Sharma., C. H. Lee., S. W. Lee.,

    Integrated Platform for Understanding Physical Prior & Task Learning,

    IEEE International Conference on Robot Intelligence Technology and Applications (2019).

  • J. H. Shin., J. H. Lee., S. Tong., S. H. Kim., S. W. Lee.,

    Designing model-based and model-free reinforcement learning tasks without human guidance,

    Multi-disciplinary Conference on Reinforcement Learning and Decision Making (2019).

  • D. J. Kim., S. W. Lee.,

    Behavioral and neural evidence for intrinsic motivation effect on reinforcement learning,

    Multi-disciplinary Conference on Reinforcement Learning and Decision Making (2019).

  • D. J. Kim., S. W. Lee.,
    Deciphering model-based and model-free reinforcement learning strategies and choices from electroencephalography,
    Multi-disciplinary Conference on Reinforcement Learning and Decision Making (2019).
  • S. J. An, B. D. Martino, and S. W. Lee.,

    Metacognitive exploration in reinforcement learning,

    Multi-disciplinary Conference on Reinforcement Learning and Decision Making (2019).

  • Y.  J. Rah., J. H. Shin., S. A. Lee.,
    Predicting subjective and objective memory recollection from prefrontal cortex activations using high-density fNIRS,
    Neuroscience (2019).

  • J. H. Shin., S. A. Lee.,

    Neural representation of episodic memory components in the prefrontal cortex measured by fNIRS,

    Neuroscience (2019)(Oral Presentation)

  • S. A. Lee.

    The binding of space and time in episodic memory,

    Flux Congress for developmental cognitive neuroscience (2019).

  • S. A. Lee.

    Spatiotemporal binding in episodic memory,

    Hippocampus symposium (2019).

  • J. H. Shin., J. H. Lee., S. Tong., S. H. Kim., S. W. Lee.,

    Designing model-based and model-free reinforcement learning tasks without human guidance,

    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019).

Domestic Conference

  • M. R. Song., S. W. Lee.

    Midbrain dopamine activity during reinforcement learning reflects bias-variance tradeoff,

    한국인공지능학회 하계학술대회 (2020).

  • M. Yang., J. H. Lee, S. W. Lee.

    Biological Reinforcement Learning via Predictive Spacetime Encoding,

    한국인공지능학회 하계학술대회 (2020).

  • J. H. Shin., J. H. Lee., S. W. Lee.

    Deep Interaction between Reinforcement Learning Algorithms and Human Reinforcement Learning,

    한국인공지능학회 하계학술대회 (2020).

  • H. Kee., C. Ahn., S. H. Oh.

    Model selection for imitation learning,

    한국인공지능학회 하계학술대회 (2020).

  • J. Park, J. G. Choy., S. H. Oh.

    Memory Efficient Reinforcement Learning for Multi-tasks with Deep Virtual Q-Networks,

    한국인공지능학회 하계학술대회 (2020).

  • N. Kim., S. H. Oh.

    Active Multi-Class Object Detection Using Deep Reinforcement Learning,

    한국인공지능학회 하계학술대회 (2020).

  • J. Y. Kim., Y.  J. Rah., S. A. Lee.

    공간에서의 경계가 아동의 일화 기억에 미치는 영향(How spatial boundaries influence children’s episodic memory),

    한국인지및생물심리학회 (2020).

  • S. A. Lee.

    How the Brain Represents Spatiotemporal Events,

    한국인지및생물심리학회 (2020).

  • S. A. Lee.

    Leveraging the Interaction Between Attention and Affect,

    한국임상심리학회 가을학술대회 (2020).

  • S. A. Lee., S. Park.

    Investigating Age-Related Decline and Compensation in Hippocampal Memory Processes Using Human Intracranial EEG,

    대한뇌기능매핑학회 추계학술대회 (2020).

  • J. H. Shin., S. A. Lee.,

    Novelty and uncertainty representation in the human brain during flexible learning,

    한국인지과학회 학술대회 (2019).

  • H. Y. Yoo., E. W. Kim., S. H. Oh.,
    중첩 희소 네트워크를 이용한 계층적인 이미지 의미론적 분할 네트워크,
    통신정보 합동학술대회 (2019).

  • J. E. Hwang., S. A. Lee.,
    Development of what, where, when components in episodic memory,
    한국인지과학회 (2019).

  • Gitae Koo., S. A. Lee.,
    Scene perception and memory: Comparison of Scene Memory Performance in Williams Syndrome and Typical Young Adults,
    한국인지과학회 (2019).

  • J. H. Shin., S. A. Lee.,
    Novelty and uncertainty representation in the human brain during flexible learning,
    한국인지과학회 (2019).

  • Y.  J. Rah., S. A. Lee.,
    The contrast between subjective and objective memory in recalling events with episodic memory components.
    한국인지과학회 (2019).

  • S. A. Lee.

    Spatial cognition and episodic memory in childhood,

    한국생애학회 (2019).

  • Y. J. Rah., S. A. Lee.,
    기억발달과 공간인지 (Development of Spatial Cognition and Episodic Memory).
    인지발달중재학회 동계학술대회 (2019).

International Collaboration

  • (KAIST-Caltech) Kim, D., Park, G. Y., O’Doherty J. P.*, Lee S. W.*

    Task complexity interacts with state-space uncertainty in the arbitration process between model-based and model-free reinforcement-learning at both behavioral and neural levels.

    Nature Communications. 10, 5738 (2019).

  • (KAIST-Caltech) O’Doherty J. P.*, Lee S. W., TadayonNejad R., Cockburn J., Iigaya K., Chrpentier C.

    Why and how the brain weights contributions from a mixture of experts.

    Neuroscience and Biobehavioral Reviews (2020) (in press) .

  • (KAIST-NYU Shanghai) Zuo, S., Wang, L., Shin, J. H., Cai, Y., Lee, S. W., Appiah, K., Zhou, Y., Kwok S. C.

    Behavioral evidence for memory replay of video episodes in the macaque.

    eLife. 9, e54519 (2020).

  • (KAIST-U Zurich) Weissengruber S.+, Lee S. W.+, O'Doherty J, Ruff C.

    Neurostimulation reveals context-dependent arbitration between model-based and model-free learning.

    Cerebral Cortex. 29 (2019).

  • (KAIST-UCL) S. J. An, B. D. Martino, and S. W. Lee*

    Metacognitive exploration in reinforcement learning.

    4th Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) (2019).

  • (KAIST-IBM) J. Yun, P. Zheng, A. Lozano, A. Aravkin and E. Yang

    Trimming the l-1 Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning.

    International Conference on Machine Learning (ICML) 36 (2019) (Oral, acceptance rate = 4.64%) .

  • (KAIST-IBM) J. Yun, A. Lozano and E. Yang

    Stochastic Gradient Methods with Block Diagonal Matrix Adaptation.

    arXiv preprint arXiv:1905.10757 (2019).

  • (KAIST-IBM) J. Yun, A. Lozano, E. Yang

    A General Family of Stochastic Proximal Gradient Methods for Deep Learning.

    arXiv preprint arXiv:2007.07484 (2020).

  • (고려대-Technische Universität Berlin) D.-O. Won, K.-R. Müller, and S.-W. Lee

    An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real world conditions.

    Science Robotics. Vol. 5, Issue 46 (2020).

  • (KAIST-Columnbia) Sang-Eon Park, Tamara Gedankein, Joshua Jacobs, Sang Ah Lee

    What human intracranial EEG reveals about the effects of aging on neurocognitive function.

    한국뇌신경과학회 (2020)

Domestic Collaboration

  • (KAIST-Humelo) B.-J. Choi, J. Hong, D. Park and S. W. Lee

    F^2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax.

    Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020) (acceptance rate = 22.4%)

  • (KAIST-Humelo) M. Elgaar, J. Park, and S. W. Lee*

    Multi-speaker and multi-domain emotional voice conversion using factorized hierarchical variational autoencoder.

    Proceeding of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2020)

  • (KAIST-Humelo) J. Park, K. Han, Y. Jeong, and S. W. Lee*

    Phonemic-level duration control using attention alignment for natural speech synthesis.

    Proceeding of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2019) (Oral)

  • (KAIST-Humelo) S. Jung, J. Park, and S. W. Lee*

    Polyphonic sound event detection using convolutional bidirectional LSTM and synthetic data-based transfer learning.

    Proceeding of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2019)

  • (KAIST-KAIST) Junghan Shin, Suyeon Heo, Sang Ah Lee, Sang Wan Lee

    Novelty and uncertainty representation in the human brain during flexible learning.

    한국인지과학회 (2019)

  • (KAIST-KAIST) S. A. Lee, S. W. Lee

    인간지능, 인공지능.

    한국인지과학회 (2019)