5 papers
SAUL: Sharpness-Aware Augmented-Lagrangian Unlearning
Jaewan Choi, Junyoung Yang, Sangdon Park
Machine unlearning in Large Language Models (LLMs) faces a critical trade-off between erasing target knowledge and preserving general utility. We propose SAUL (Sharpness-Aware Augm…
LP5X-PIM Sim: A High-Fidelity HW/SW Integrated Simulator for LPDDR5X-PIM
SangHoon Cha, Jaewan Choi, Byeongho Kim +3
This tech note describes the architecture and execution results of the LPDDR5X-PIM simulator, developed by Samsung Electronics. Based on the latest research and internal specificat…
GuardMarkGS: Unified Ownership Tracing and Edit Deterrence for 3D Gaussian Splatting
Utae Jeong, Jaewan Choi, Junseok Lee +4
3D Gaussian Splatting (3DGS) is becoming a practical representation for novel view synthesis, but its growing adoption, together with rapid advances in instruction-driven 3DGS edit…
WaTeRFlow: Watermark Temporal Robustness via Flow Consistency
Utae Jeong, Sumin In, Hyunju Ryu +5
Image watermarking supports authenticity and provenance, yet many schemes are still easy to bypass with various distortions and powerful generative edits. Deep learning-based water…
Duplex: A Device for Large Language Models with Mixture of Experts, Grouped Query Attention, and Continuous Batching
Sungmin Yun, Kwanhee Kyung, Juhwan Cho +6
Large language models (LLMs) have emerged due to their capability to generate high-quality content across diverse contexts. To reduce their explosively increasing demands for compu…