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Taesun Yeom

4 papers hereh-index 213 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG4

identity via Semantic Scholar / OpenAlex

collaborators

4 papers

cs.LG2026

Activation Quantization of Vision Encoders Needs Prefixing Registers

Seunghyeon Kim, Taesun Yeom, Jinho Kim +3

Large pretrained vision encoders are central to multimodal intelligence, powering applications from on-device vision processing to vision-language models. Since these applications…

cs.LG2026

Over-Alignment vs Over-Fitting: The Role of Feature Learning Strength in Generalization

Taesun Yeom, Taehyeok Ha, Jaeho Lee

Feature learning strength (FLS), i.e., the inverse of the effective output scaling of a model, plays a critical role in shaping the optimization dynamics of neural nets. While its…

cs.LG2026

Fast Training of Sinusoidal Neural Fields via Scaling Initialization

Taesun Yeom, Sangyoon Lee, Jaeho Lee

Neural fields are an emerging paradigm that represent data as continuous functions parameterized by neural networks. Despite many advantages, neural fields often have a high traini…

cs.LG2025

On the Internal Representations of Graph Metanetworks

Taesun Yeom, Jaeho Lee

Weight space learning is an emerging paradigm in the deep learning community. The primary goal of weight space learning is to extract informative features from a set of parameters…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.