3 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…