Chulhee Yun

1. Are Transformers universal approximators of sequence-to-sequence functions?
   2019/12/20 by Chulhee Yun, Yun, Chulhee, Srinadh Bhojanapalli +7 · 1 voice · 45 citations
   Computer Science · #Fuzzy Logic and Control Systems #Natural Language Processing Techniques #Neural Networks and Applications #Software Engineering Research #Speech Recognition and Synthesis #Topic Modeling #cs.LG #stat.ML
2. Low-Rank Bottleneck in Multi-head Attention Models
   2020/02/17 by Srinadh Bhojanapalli, Chulhee Yun, Bhojanapalli, Srinadh +7 · 15 citations
   Computer Science · #Topic Modeling #Advanced Neural Network Applications #Multimodal Machine Learning Applications
3. Linear attention is (maybe) all you need (to understand transformer optimization)
   2023/10/02 by Kwangjun Ahn, Ahn, Kwangjun, Xiang Cheng +9 · 18 citations
   Engineering · #Artificial Intelligence (cs.AI) #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC)
4. Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity
   2018/10/17 by Chulhee Yun, Suvrit Sra, Yun, Chulhee +3 · 7 citations
   Computer Science · Engineering · #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques #Advanced Memory and Neural Computing
5. PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
   2023/06/19 by Hojoon Lee, Lee, Hojoon, Hanseul Cho +13 · 8 citations
   Computer Science · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Reinforcement Learning in Robotics
6. Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima
   2023/06/16 by Dongkuk Si, Chulhee Yun, Si, Dongkuk +1 · 5 citations
   Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
7. Does SGD really happen in tiny subspaces?
   2024/05/25 by Minhak Song, Kwangjun Ahn, Song, Minhak +3 · 7 citations
   Medicine · #Systemic Sclerosis and Related Diseases
8. Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond
   2021/10/20 by Chulhee Yun, Shashank Rajput, Yun, Chulhee +3 · 2 citations
   Computer Science · Engineering · #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques #Privacy-Preserving Technologies in Data
9. Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
   2023/10/28 by Jung-Hyun Lee, Hanseul Cho, Lee, Junghyun +5 · 2 citations
   Engineering · Computer Science · #Sparse and Compressive Sensing Techniques #Face and Expression Recognition #Blind Source Separation Techniques
10. Are deep ResNets provably better than linear predictors?
    2019/07/09 by Chulhee Yun, Yun, Chulhee, Suvrit Sra +3 · 1 citation
    Computer Science · Engineering · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #FOS: Mathematics #Integrated Circuits and Semiconductor Failure Analysis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
11. Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
    2025/02/10 by Yeseul Cho, Cho, Yeseul, Bong-Ho Shin +5 · 4 citations
    Computer Science · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Neural Networks and Applications
12. Trajectory Alignment: Understanding the Edge of Stability Phenomenon via Bifurcation Theory
    2023/07/09 by Minhak Song, Chulhee Yun, Song, Minhak +1 · 2 citations
    Neuroscience · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Force Microscopy Techniques and Applications #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural dynamics and brain function #Optimization and Control (math.OC) #stochastic dynamics and bifurcation
13. Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond
    2023/03/13 by Jaeyoung Cha, Jaewook Lee, Cha, Jaeyoung +3 · 1 citation
    Computer Science · Engineering · Mathematics · #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference
14. Provable Benefit of Cutout and CutMix for Feature Learning
    2024/10/31 by Junsoo Oh, Oh, Junsoo, Chulhee Yun +1 · 1 citation
    Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification
15. DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity
    2024/10/30 by Bong-Ho Shin, Junsoo Oh, Shin, Baekrok +5 · 1 citation
    Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural Networks and Applications