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Taesun Yeom
I'm a second-year M.S.–Ph.D. student at EffL (Efficient Learning Lab), POSTECH (advisor: Prof. Jaeho Lee).
For my research, I primarily focus on understanding various phenomena that arise in deep neural networks from a theoretical perspective.
My recent research interests include (1) Implicit biases and learning dynamics of neural networks, (2) Deep learning with infinite-dimensional functions (e.g., neural fields), and (3) Efficient deep learning.
Before joining EffL, I received my bachelor's degree in Mechanical Engineering from Chung-Ang University and worked closely with Prof. Minhyeok Lee and Prof. Seokwon Lee.
Email | GitHub | Google Scholar | LinkedIn | CV
Publications
Activation Quantization of Vision Encoders Needs Prefixing Registers
Seunghyeon Kim, Taesun Yeom, Jinho Kim, Wonpyo Park, Kyuyeun Kim, and Jaeho Lee
Under review, 2025
arxiv
Over-Alignment vs Over-Fitting: The Role of Feature Learning Strength in Generalization
Taesun Yeom, Taehyeok Ha, and Jaeho Lee
ICML, 2026
arxiv
Fast Training of Sinusoidal Neural Fields via Scaling Initialization
Taesun Yeom*, Sangyoon Lee*, and Jaeho Lee
ICLR, 2025
arxiv | code | openreview
DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion
Taesun Yeom, Chanhoe Gu, and Minhyeok Lee
IEEE Access, 2024
paper | code
Superstargan: Generative adversarial networks for image-to-image translation in large-scale domains
Kanghyeok Ko, Taesun Yeom, and Minhyeok Lee
Neural Networks, 2023
paper | code
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