Taesun Yeom

I'm a first-year M.S.-Ph.D. student at EffL (Efficient Learning Lab), POSTECH (advisor: Prof. Jaeho Lee). My research primarily focuses on gaining a deeper understanding of neural nets by developing theories that closely align with empirical phenomena.

Recently, i have been particularly interested in neural fields, implicit regularization, geometric deep learning, and learning dynamics. Feel free to reach out if you’re interested in collaborating on any of these research topics.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

Publications

On the Internal Representations of Graph Metanetworks

What do graph metanetworks learn?

Fast Training of Sinusoidal Neural Fields via Scaling Initialization

We propose a simple yet effective approach for accelerating neural field training.

DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion

We propose a dual-diffusion process, which aims to reduce overfitting of class-conditional GANs.

Superstargan: Generative adversarial networks for image-to-image translation in large-scale domains

Enabling GANs for image-to-image translation in large-scale domains.

Education

Work experience

Design and source code from Jon Barron's website