3 citations · 3 across the 7 of their papers we have counts for
10 papers
HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning
Donggyun Kim, Jack Lu, Chanwoo Kim +2
Long-form thinking traces can substantially improve the multi-step reasoning performance of large language models (LLMs), but they introduce high inference-time overhead, with late…
Universal Few-Shot Spatial Control for Diffusion Models
Kiet T. Nguyen, Chanhyuk Lee, Donggyun Kim +2
Spatial conditioning in pretrained text-to-image diffusion models has significantly improved fine-grained control over the structure of generated images. However, existing control…
HyperFlow: Gradient-Free Emulation of Few-Shot Fine-Tuning
Donggyun Kim, Chanwoo Kim, Seunghoon Hong
While test-time fine-tuning is beneficial in few-shot learning, the need for multiple backpropagation steps can be prohibitively expensive in real-time or low-resource scenarios. T…
AdaRank: Adaptive Rank Pruning for Enhanced Model Merging
Chanhyuk Lee, Jiho Choi, Chanryeol Lee +2
Model merging has emerged as a promising approach for unifying independently fine-tuned models into an integrated framework, significantly enhancing computational efficiency in mul…
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
Seongwoong Cho, Donggyun Kim, Jinwoo Lee +1
Generalizing across robot embodiments and tasks is crucial for adaptive robotic systems. Modular policy learning approaches adapt to new embodiments but are limited to specific tas…
Revisiting Weight Averaging for Model Merging
Jiho Choi, Donggyun Kim, Chanhyuk Lee +1
Model merging aims to build a multi-task learner by combining the parameters of individually fine-tuned models without additional training. While a straightforward approach is to a…
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild
Donggyun Kim, Seongwoong Cho, Semin Kim +2
Large language models have evolved data-efficient generalists, benefiting from the universal language interface and large-scale pre-training. However, constructing a data-efficient…
Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching
Donggyun Kim, Jinwoo Kim, Seongwoong Cho +2
Dense prediction tasks are a fundamental class of problems in computer vision. As supervised methods suffer from high pixel-wise labeling cost, a few-shot learning solution that ca…
Multi-Task Neural Processes
Donggyun Kim, Seongwoong Cho, Wonkwang Lee +1
Neural Processes (NPs) consider a task as a function realized from a stochastic process and flexibly adapt to unseen tasks through inference on functions. However, naive NPs can mo…
High-Fidelity Synthesis with Disentangled Representation
Wonkwang Lee, Donggyun Kim, Seunghoon Hong +1
Learning disentangled representation of data without supervision is an important step towards improving the interpretability of generative models. Despite recent advances in disent…