4 citations · 7 across the 10 of their papers we have counts for
11 papers
Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random
Gyuwon Sim, Sumin Lee, Heesun Bae +5
Missing data frequently arises across diverse domains, including time-series and image domains. In the real world, missing occurrences often depend on the unobservable values thems…
Semantic-aware Wasserstein Policy Regularization for Large Language Model Alignment
Byeonghu Na, Hyungho Na, Yeongmin Kim +4
Large language models (LLMs) are commonly aligned with human preferences using reinforcement learning from human feedback (RLHF). In this method, LLM policies are generally optimiz…
Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models
Byeonghu Na, Minsang Park, Gyuwon Sim +6
Text-to-image diffusion models rely on text embeddings from a pre-trained text encoder, but these embeddings remain fixed across all diffusion timesteps, limiting their adaptabilit…
Preference Optimization by Estimating the Ratio of the Data Distribution
Yeongmin Kim, Heesun Bae, Byeonghu Na +1
Direct preference optimization (DPO) is widely used as a simple and stable method for aligning large language models (LLMs) with human preferences. This paper investigates a genera…
Distilling Dataset into Neural Field
Donghyeok Shin, HeeSun Bae, Gyuwon Sim +2
Utilizing a large-scale dataset is essential for training high-performance deep learning models, but it also comes with substantial computation and storage costs. To overcome these…
Unknown Domain Inconsistency Minimization for Domain Generalization
Seungjae Shin, HeeSun Bae, Byeonghu Na +2
The objective of domain generalization (DG) is to enhance the transferability of the model learned from a source domain to unobserved domains. To prevent overfitting to a specific…
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning
HeeSun Bae, Seungjae Shin, Byeonghu Na +1
For learning with noisy labels, the transition matrix, which explicitly models the relation between noisy label distribution and clean label distribution, has been utilized to achi…
Label-Noise Robust Diffusion Models
Byeonghu Na, Yeongmin Kim, HeeSun Bae +4
Conditional diffusion models have shown remarkable performance in various generative tasks, but training them requires large-scale datasets that often contain noise in conditional…
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior
Youngjae Cho, HeeSun Bae, Seungjae Shin +3
Recent Vision-Language Pretrained (VLP) models have become the backbone for many downstream tasks, but they are utilized as frozen model without learning. Prompt learning is a meth…
Loss-Curvature Matching for Dataset Selection and Condensation
Seungjae Shin, Heesun Bae, Donghyeok Shin +2
Training neural networks on a large dataset requires substantial computational costs. Dataset reduction selects or synthesizes data instances based on the large dataset, while mini…
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
HeeSun Bae, Seungjae Shin, Byeonghu Na +3
Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization of a classifier by making the classifier over-fitted to noisy labels. Existing…