6 papers
MoNe: Modular Neural Memory for Efficient Long Context Inference
Wonguk Cho, Kyubyung Chae, Tribhuvanesh Orekondy +6
We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retraining. MoNe reads context in f…
ReCo: Reweighting GRPO Against Distributional Concentration
Junoh Park, Junseo Hwang, Wonguk Cho +1
Group Relative Policy Optimization (GRPO) has become a standard reinforcement learning method for post-training language models. Recent work shows that GRPO can reduce the base mod…
PiCa: Parameter-Efficient Fine-Tuning with Column Space Projection
Junseo Hwang, Wonguk Cho, Taesup Kim
Fine-tuning large foundation models is essential for building expert models tailored to specialized tasks and domains, but fully updating billions of parameters is computationally…
Hollowed Net for On-Device Personalization of Text-to-Image Diffusion Models
Wonguk Cho, Seokeon Choi, Debasmit Das +4
Recent advancements in text-to-image diffusion models have enabled the personalization of these models to generate custom images from textual prompts. This paper presents an effici…
Overcoming Data Inequality across Domains with Semi-Supervised Domain Generalization
Jinha Park, Wonguk Cho, Taesup Kim
While there have been considerable advancements in machine learning driven by extensive datasets, a significant disparity still persists in the availability of data across various…
Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning
Wonguk Cho, Jinha Park, Taesup Kim
Continual domain shift poses a significant challenge in real-world applications, particularly in situations where labeled data is not available for new domains. The challenge of ac…