Search Results - Yun, Chulhee

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  1. 1
    Face detection using Local Hybrid Patterns

    Face detection using Local Hybrid Patterns by Chulhee Yun, Donghoon Lee, Yoo, Chang D.

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  2. 2
    Quantitative analysis of the role of nanohydroxyapatite (nHA) on 3D-printed PCL/nHA composite scaffolds

    Quantitative analysis of the role of nanohydroxyapatite (nHA) on 3D-printed PCL/nHA composite scaffolds by Kim, Myoung Hwan, Yun, Chulhee, Chalisserry, Elna Paul, Lee, Yong Wook, Kang, Hyun Wook, Park, Sang-Hyug, Jung, Won-Kyo, Oh, Junghwan, Nam, Seung Yun

    Published in Materials letters
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  3. 3
    Provable Benefit of Cutout and CutMix for Feature Learning

    Provable Benefit of Cutout and CutMix for Feature Learning by Oh, Junsoo, Yun, Chulhee

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  4. 4
    Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima

    Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima by Si, Dongkuk, Yun, Chulhee

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  5. 5
    Trajectory Alignment: Understanding the Edge of Stability Phenomenon via Bifurcation Theory

    Trajectory Alignment: Understanding the Edge of Stability Phenomenon via Bifurcation Theory by Song, Minhak, Yun, Chulhee

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  6. 6
    An implementation of computer vision technique for an edutainment robot with a visual programming language

    An implementation of computer vision technique for an edutainment robot with a visual programming language by Chulhee Yun, Jaegon Ahn, Yeon-Ho Kim

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  7. 7
    Provable Benefit of Mixup for Finding Optimal Decision Boundaries

    Provable Benefit of Mixup for Finding Optimal Decision Boundaries by Oh, Junsoo, Yun, Chulhee

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  8. 8
    SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization

    SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization by Cho, Hanseul, Yun, Chulhee

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  9. 9
    Fundamental Benefit of Alternating Updates in Minimax Optimization

    Fundamental Benefit of Alternating Updates in Minimax Optimization by Lee, Jaewook, Cho, Hanseul, Yun, Chulhee

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  10. 10
    On the Training Instability of Shuffling SGD with Batch Normalization

    On the Training Instability of Shuffling SGD with Batch Normalization by Wu, David X, Yun, Chulhee, Sra, Suvrit

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  11. 11
    Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond

    Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond by Cha, Jaeyoung, Lee, Jaewook, Yun, Chulhee

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  12. 12
    DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity

    DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity by Shin, Baekrok, Oh, Junsoo, Cho, Hanseul, Yun, Chulhee

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  13. 13
    Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

    Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count by Cho, Hanseul, Cha, Jaeyoung, Bhojanapalli, Srinadh, Yun, Chulhee

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  14. 14
    Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint

    Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint by Lee, Junghyun, Cho, Hanseul, Se-Young, Yun, Yun, Chulhee

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  15. 15
    Large Catapults in Momentum Gradient Descent with Warmup: An Empirical Study

    Large Catapults in Momentum Gradient Descent with Warmup: An Empirical Study by Prin Phunyaphibarn, Lee, Junghyun, Wang, Bohan, Zhang, Huishuai, Yun, Chulhee

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  16. 16
    Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond

    Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond by Yun, Chulhee, Rajput, Shashank, Sra, Suvrit

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  17. 17
    A Unifying View on Implicit Bias in Training Linear Neural Networks

    A Unifying View on Implicit Bias in Training Linear Neural Networks by Yun, Chulhee, Krishnan, Shankar, Mobahi, Hossein

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  18. 18
    Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure

    Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure by Cho, Hanseul, Cha, Jaeyoung, Awasthi, Pranjal, Bhojanapalli, Srinadh, Gupta, Anupam, Yun, Chulhee

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  19. 19
    Can Single-Shuffle SGD be Better than Reshuffling SGD and GD?

    Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? by Yun, Chulhee, Sra, Suvrit, Jadbabaie, Ali

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  20. 20
    Linear attention is (maybe) all you need (to understand transformer optimization)

    Linear attention is (maybe) all you need (to understand transformer optimization) by Ahn, Kwangjun, Cheng, Xiang, Song, Minhak, Yun, Chulhee, Jadbabaie, Ali, Sra, Suvrit

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