Research

  • S. -H. Lee, et al.
    HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised.,

    Neural Information Processing Systems (NeurIPS) (2022)

  • P. Bertens, et al.
    Emergence of Hierarchical Layers in a Single Sheet of Self-Organizing Spiking Neuron.,

    Neural Information Processing Systems (NeurIPS) (2022)

  • S. Moon, J. Lee, H. O. Song.
    Rethinking Value Function Learning for Generalization in Reinforcement Learning.,

    Neural Information Processing Systems (NeurIPS) (2022)

  • I. Hwang, et al.
    SelecMix: Debiased Learning by Contradicting-pair Sampling.,

    Neural Information Processing Systems (NeurIPS) (2022)

  • D. -S. Han, et al.
    Robust Imitation via Mirror Descent Inverse Reinforcement Learning.,

    Neural Information Processing Systems (NeurIPS) (2022)

  • W. -S. Choi, et al.
    DUEL: Adaptive Duplicate Elimination on Working Memory for Self-Supervised Learning.,

    Neural Information Processing Systems (NeurIPS) (2022)

  • S. Moon, G. An, H. O. Song.
    Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks.,

    AAAI Conference on Artificial Intelligence (AAAI) (2022)

  • J. Park, H. Shim, E. Yang
    Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation.,

    AAAI Conference on Artificial Intelligence (AAAI) (2022)

  • K. Park, H. Oh
    VECA: A New Benchmark and Toolkit for General Cognitive Development.,

    AAAI Conference on Artificial Intelligence (AAAI) (2022)

  • J. Song, J. Park, E. Yang
    TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification.,

    International Conference on Machine Learning (ICML) (2022)

  • J.-S.Kim, J.-H.Lee, B.-T.Zhang
    Smooth-Swap: A Simple Enhancement for Face-Swapping With Smoothness.,

    In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

  • T. Vu, et al.
    SoftGroup for 3D Instance Segmentation on Point Clouds.,

    In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

  • M. H. Ha, H. Chi, S. Chi, S. W. Lee, Q. Huang, K. Ramani
    InfoGCN: Representation Learning for Human Skeleton-based Action Recognition.,

    In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

  • T. Kwon, M. Jeong, E. -S. Ko, Y. Lee
    Captivate! Contextual Language Guidance for Parent–Child Interaction.,

    Conference on Human Factors in Computing Systems (CHI) (2022)

  • G. -H. Lee, M. -J. Kim, M. Lee, B. -T. Zhang
    From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching Agent.,

    In Proceedings of the 2022 IEEE International Conference on Robotics and Automation (ICRA) (2022)

  • J. Park, J. Song, E. Yang
    GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification.

    International Conference on Learning Representations (ICLR) (2022)

  • S. Kim, et al.
    Few-Shot Object Detection with Proposal Balance Refinement.,

    International Conference on Pattern Recognition (ICPR) (2022)

  • M. Kang, et al.
    Grasp Planning for Occluded Objects in a Confined Space with Lateral View Using Monte Carlo Tree Search.,

    IEEE/RSJ International Conference on Intelligent Robots and Systems (2022)

  • J. G. Choy, et al.
    Unsupervised 3D Link Segmentation of Articulated Objects with a Mixture of Coherent Point Drift.,

    IEEE/RSJ International Conference on Intelligent Robots and Systems (2022)

  • T. Ha, et al.
    RIANet: Road Graph and Image Attention Network for Urban Autonomous Driving.,

    IEEE/RSJ International Conference on Intelligent Robots and Systems (2022)

  • J. RYU, M. H. Ha, S. W. Lee
    Generalizable perceptual embedding by noise-tuning alignment.,

    Organization for Computational Neurosciences (2022)

  • Y. J. Rah, S. A. Lee
    Differential effects of expectancy violation and visual salience on infants’ and toddlers’ associative learning.,

    Budapest CEU Conference on Cognitive Development (2022)

  • S. A. Lee
    Breaking the space-time continuum: How spatial boundaries structure our event memories.,

    Neuroscience 2022 (2022)

  • J. H. Shin, et al.
    In silico manipulation of human cortical computation underlying goal-directed learning.,

    Computational and Systems Neuroscience (COSYNE) (2022)

  • S. J. An, et al.
    Rethinking Tolman's latent learning with metacognitive exploration.,

    Computational and Systems Neuroscience (COSYNE) (2022)

  • S. J. An, et al.
    How human metacognitive exploration improves reinforcement learning in a sparse reward environment.,

    Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) (2022)

  • Y. Sung, et al.
    Uncertainty and goal embeddings in the lateral prefrontal cortex guide flexible and stable reinforcement learning.,

    Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) (2022)

  • Y. Kang, et al.
    Meta-BCI: Perspectives on a role of self-supervised learning in meta brain computer interface .,

    10th International Winter Conference on Brain-Computer Interface (BCI) (2022)

  • S. J. An, et al.
    Learning state-space uncertainty, but not value uncertainty, is sufficient for metacognitive exploration.,

    From Neuroscience to Artificially Intelligent Systems (NAISys) (2022)

  • Y. -J. Heo, E. -S. Kim, W. -S. Choi, B. -T. Zhang
    Hypergraph Transformer: Weakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering.,

    In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL) (2022)

  • C. Lee, et al.
    PlaceNet: Neural Spatial Representation Learning with Multimodal Attention.,

    the 31st International Joint Conference on Artificial Intelligence (IJCAI) (2022)

  • J. H. Chai, et al.
    Mother’s initiative role in conversational exchanges and its effect on children’s language outcome.,

    The International Congress of Infant Studies (ICIS) (2022)

  • J. Jung, et al.
    The effect of socioeconomic status on early vocabulary size in South Korean children.,

    The International Congress of Infant Studies (ICIS) (2022)

  • E. -S. Ko, et al.
    Adaptation of maternal speech in statistical word segmentation of Korean.,

    The International Congress of Infant Studies (ICIS) (2022)

  • E. -S. Ko, et al.
    Korean mothers' strategies to place nouns in the utterance-final position: Comparison to American child-directed speech and Korean adult-directed speech.,

    The International Congress of Infant Studies (ICIS) (2022)

  • E. -S. Ko, et al.
    Phonological variation in child-directed speech is modulated by lexical frequency.,

    Phonological Society of Japan (PhSJ) (2022)

  • G. An, S. Moon, J.-H. Kim, H. O. Song
    Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble,

    Conference on Neural Information Processing Systems (NeurIPS) (2021)

  • K. Kim, et al.
    Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning,

    Conference on Neural Information Processing Systems (NeurIPS) (2021)

  • J. Yun, A. Lozano, E. Yang
    Adaptive Proximal Gradient Methods for Structured Neural Networks,

    Conference on Neural Information Processing Systems (NeurIPS) (2021)

  • G. Y. Park, S. W. Lee  
    Reliably Fast Adversarial Training via Latent Adversarial Perturbation,

    International Conference on Computer Vision (ICCV) (2021) (Oral Presentation)

  • G. Y. Park, S. W. Lee
    Information-theoretic regularization for Multi-source Domain Adaptation,

    International Conference on Computer Vision (ICCV) (2021)

  • G. H. Lee, S. -W. Lee
    Uncertainty-Aware Human Mesh Recovery from Video by Learning Part-Based 3D Dynamics,

    International Conference on Computer Vision (ICCV) (2021)

  • T. Kim, I. Hwang, H.-D. Lee, H. Kim, W.-S. Choi, J. Lim, B.-T. Zhang
    Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning,

    International Conference on Machine Learning (ICML) (2021)

  • 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)

  • 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)

  • Y-J. Cha, S. W. Lee,
    Human Uncertainty Inference via Deterministic Ensemble Neural Networks,

    AAAI Conference on Artificial Intelligence (AAAI) (2021)

  • 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)

  • E. S-. Ko, et al.
     Mothers’ use of tactile cues for word learning is attuned to infants’ development,

    Boston University Conference on Language Development (BUCLD46) (2021)

  • E. S-. Ko, M. McDonald
    The efficacy of book reading in infants’ word learning is mediated by child-directed speech,

    Asia Pacific Babylab Constellation (ABC) (2021)

  • E. S-. Ko, M. McDonald
    The eyes of preverbal infants reveal the effects of book reading on word learning,

    The Society for Research in Child Development (SRCD) (2021)

  • H. Kee, et al.
    Decomposed Q-learning for Non-prehensible Rearrangement Problem,

    The Inteternational Conference on Control, Automation and Systems (ICCAS) (2021)

  • A. Seo, et al.
    Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering,

    ACL-IJCNLP 2021 (2021)

  • G. -C. Kang, et al.
    Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer,

    Conference on Empirical Methods in Natural Language Processing (EMNLP) (2021)

  • B. Bebensee, 
    Co-attentional Transformers for Story-Based Video Understanding,

    International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2021)

  • J. Song, H. Shim,E. Yang
    Mutually-Constrained Monotonic Multihead Attention For Online ASR,

    International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2021)

  • J. Song, H. Shim, E. Yang
    LEARNING HOW LONG TO WAIT : ADAPTIVELY-CONSTRAINED MONOTONICMULTIHEAD ATTENTION FOR STREAMING ASR,

    IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) (2021)

  • G. Park, G. Kim, E. Yang
    Distilling Linguistic Context for Language Model Compression,

    Conference on Empirical Methods in Natural Language Processing (EMNLP) (2021)

  • Y. Sung, S. W. Lee.
    Goal and context embeddings of the lateral prefrontal cortex during reinforcement learning,

    Neuroscience 2021 (2021)

  • M. R. Song, et al.
     Midbrain dopamine activity during reinforcement learning reflects bias-variance tradeoff,

    Computational and Systems Neuroscience (COSYNE) (2021)

  • D. J. Kim, S. W. Lee.
    Decoding learning strategies from EEG signals provides generalizable features for decoding decision,

    9th International Winter Conference on Brain-Computer Interface (BCI) (2021)

  • H. Joo, S. W. Lee.
    Estimating the level of inference using an order-mimic agent,

    the 6th Asian Conference on Pattern Recognition (ACPR) (2021)

  • S. J. An, S. W. Lee.
    Metacognition guides near-optimal exploration of a large state space with sparse rewards,

    Computational and Systems Neuroscience (COSYNE) (2021)

  • J. H. Shin, S. W. Lee.
    In silico manipulation of human cortical computation underlying goal-directed learning,

    Workshop on Human and Machine Decisions (WHMD) (2021)

  • S. A. Lee, 
     How Perceptual Processing of Environmental Cues Contributes to Hippocampal Memory Across Species,

    Park City Winter Conference on the Neurobiology of Learning and Memory (2021)

  • J. Park., et al.
    Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents,

    23rd ACM International Conference on Multimodal Interaction (ICMI) (2021) (Oral Presentation)

  • J. Lim., et al.
    Devil’s Advocate: Novel Boosting Ensemble Method from Psychological Findings for Text Classification,

    Conference on Empirical Methods in Natural Language Processing (EMNLP) (2021)

  • J. Lim., et al.
    Passive Versus Active: Frameworks of Active Learning for Linking Humans to Machines,

    Cognitive Science Society (CogSci) (2021)

  • 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).

CNAI KAIST

Center for Neuroscience-inspired Artificial Intelligence

402-12, W13 (Meta Convergence Bldg. I, 서측 메타융합관)
KAIST 291, Daehak-ro, Yuseong-gu,
Daejeon 34141, Republic of Korea

cnai@kaist.ac.kr

+82-42-350-6574

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