7 citations · 9 across the 15 of their papers we have counts for
7 papers · 1 filter
Superficial Safety Alignment Hypothesis
Jianwei Li, Jung-Eun Kim
As large language models (LLMs) are overwhelmingly more and more integrated into various applications, ensuring they generate safe responses is a pressing need. Previous studies on…
Estimating Environmental Cost Throughout Model's Adaptive Life Cycle
Vishwesh Sangarya, Richard Bradford, Jung-Eun Kim
With the rapid increase in the research, development, and application of neural networks in the current era, there is a proportional increase in the energy needed to train and use…
Representation Magnitude has a Liability to Privacy Vulnerability
Xingli Fang, Jung-Eun Kim
The privacy-preserving approaches to machine learning (ML) models have made substantial progress in recent years. However, it is still opaque in which circumstances and conditions…
Center-Based Relaxed Learning Against Membership Inference Attacks
Xingli Fang, Jung-Eun Kim
Membership inference attacks (MIAs) are currently considered one of the main privacy attack strategies, and their defense mechanisms have also been extensively explored. However, t…
Aggregate Representation Measure for Predictive Model Reusability
Vishwesh Sangarya, Richard Bradford, Jung-Eun Kim
In this paper, we propose a predictive quantifier to estimate the retraining cost of a trained model in distribution shifts. The proposed Aggregated Representation Measure (ARM) qu…
ReffAKD: Resource-efficient Autoencoder-based Knowledge Distillation
Divyang Doshi, Jung-Eun Kim
In this research, we propose an innovative method to boost Knowledge Distillation efficiency without the need for resource-heavy teacher models. Knowledge Distillation trains a sma…
The Over-Certainty Phenomenon in Modern Test-Time Adaptation Algorithms
Fin Amin, Jung-Eun Kim
When neural networks are confronted with unfamiliar data that deviate from their training set, this signifies a domain shift. While these networks output predictions on their input…