8 papers
Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks
JuneHyoung Kwon, MiHyeon Kim, Eunju Lee +3
While Large Vision-Language Models (LVLMs) offer powerful capabilities, they pose privacy risks by unintentionally memorizing sensitive personal information. Current unlearning ben…
HyCal: A Training-Free Prototype Calibration Method for Cross-Discipline Few-Shot Class-Incremental Learning
Eunju Lee, MiHyeon Kim, JuneHyoung Kwon +4
Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) methods assume homogeneous domains a…
Easy to Learn, Yet Hard to Forget: Towards Robust Unlearning Under Bias
JuneHyoung Kwon, MiHyeon Kim, Eunju Lee +3
Machine unlearning, which enables a model to forget specific data, is crucial for ensuring data privacy and model reliability. However, its effectiveness can be severely undermined…
Position on LLM-Assisted Peer Review: Addressing Reviewer Gap through Mentoring and Feedback
JungMin Yun, JuneHyoung Kwon, MiHyeon Kim +1
The rapid expansion of AI research has intensified the Reviewer Gap, threatening the peer-review sustainability and perpetuating a cycle of low-quality evaluations. This position p…
IM-BERT: Enhancing Robustness of BERT through the Implicit Euler Method
Mihyeon Kim, Juhyoung Park, Youngbin Kim
Pre-trained Language Models (PLMs) have achieved remarkable performance on diverse NLP tasks through pre-training and fine-tuning. However, fine-tuning the model with a large numbe…
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini, Omri Abend, Dina Acklin +105
This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2…