16 citations · 18 across the 6 of their papers we have counts for
6 papers
The Price of Thinking: Reasoning Effort as a Model-Specific API Contract
Yeabin Moon
API buyers purchase a dated contract, not a model name alone: the contract includes the requested and served model, reasoning-effort term or its omission, output rail, service prod…
TRAP: Benchmark for Task-completion and Resistance to Active Privacy-extraction
Moon Ye-Bin, Nam Hyeon-Woo, Baek Seong-Eun +2
Agents are increasingly deployed in document-intensive workflows where sensitive private information is not an edge case but a routine input, e.g., an agent booking a flight needs…
SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems
Moon Ye-Bin, Nam Hyeon-Woo, Wonseok Choi +3
Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully cu…
TextManiA: Enriching Visual Feature by Text-driven Manifold Augmentation
Moon Ye-Bin, Jisoo Kim, Hongyeob Kim +2
We propose TextManiA, a text-driven manifold augmentation method that semantically enriches visual feature spaces, regardless of class distribution. TextManiA augments visual data…
HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields
Kim Jun-Seong, Kim Yu-Ji, Moon Ye-Bin +1
We propose high dynamic range (HDR) radiance fields, HDR-Plenoxels, that learn a plenoptic function of 3D HDR radiance fields, geometry information, and varying camera settings inh…
FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning
Nam Hyeon-Woo, Moon Ye-Bin, Tae-Hyun Oh
In this work, we propose a communication-efficient parameterization, FedPara, for federated learning (FL) to overcome the burdens on frequent model uploads and downloads. Our metho…