2 citations · 2 across the 6 of their papers we have counts for
6 papers
Gradient Descent with Large Step Size Restores Symmetry in Deep Linear Networks with Multi-Pathway
Hee-Sung Kim, Sungyoon Lee
Recent analyses of multi-pathway Deep Linear Networks use Gradient Flow to predict a "winner-takes-all" specialization in which path symmetry breaks and each feature concentrates i…
Inconsistency-Aware Minimization: Improving Generalization with Unlabeled Data
Hee-Sung Kim, Hyeonseong Kim, Sungyoon Lee
Estimating the generalization gap and developing optimization methods that improve generalization are crucial for deep learning models, for both theoretical understanding and pract…
Parallel Tempering Initial Sampling in Inference-Time Reward Alignment
Myeongjun Oh, Gwangho Kim, Sungyoon Lee
Inference-time reward alignment steers pretrained diffusion and flow-based generative models to satisfy user-specified rewards without retraining. Recently, Sequential Monte Carlo…
Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences
Gwangho Kim, Sungyoon Lee
Diffusion models can unintentionally memorize training samples, raising concerns about privacy and copyright. While recent methods can detect memorization, they often rely on globa…
Bridged Adversarial Training
Hoki Kim, Woojin Lee, Sungyoon Lee +1
Adversarial robustness is considered as a required property of deep neural networks. In this study, we discover that adversarially trained models might have significantly different…
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization
Sungyoon Lee, Hoki Kim, Jaewook Lee
Deep learning is vulnerable to adversarial examples. Many defenses based on randomized neural networks have been proposed to solve the problem, but fail to achieve robustness again…