6 papers
3-Model Speculative Decoding
Sanghyun Byun, Mohanad Odema, Jung Ick Guack +3
Speculative Decoding (SD) accelerates inference in large language models by using a smaller draft model to propose tokens, which are then verified by a larger target model. However…
Unifying Vision-Language Latents for Zero-label Image Caption Enhancement
Sanghyun Byun, Jung Ick Guack, Mohanad Odema +3
Vision-language models (VLMs) achieve remarkable performance through large-scale image-text pretraining. However, their reliance on labeled image datasets limits scalability and le…
CARVQ: Corrective Adaptor with Group Residual Vector Quantization for LLM Embedding Compression
Dayin Gou, Sanghyun Byun, Nilesh Malpeddi +4
Large Language Models (LLMs) typically rely on a large number of parameters for token embedding, leading to substantial storage requirements and memory footprints. In particular, L…
APCE: Adaptive Progressive Context Expansion for Long Context Processing
Baisub Lee, Sanghyun Byun, Mohanad Odema +3
Deploying useful Long-Context Transformer Models (LCTMs) requires addressing two key challenges: (1) A growing memory footprint due to quadratic self-attention and linear KV-cache…
OneNet: A Channel-Wise 1D Convolutional U-Net
Sanghyun Byun, Kayvan Shah, Ayushi Gang +3
Many state-of-the-art computer vision architectures leverage U-Net for its adaptability and efficient feature extraction. However, the multi-resolution convolutional design often l…
MultiDepth: Multi-Sample Priors for Refining Monocular Metric Depth Estimations in Indoor Scenes
Sanghyun Byun, Jacob Song, Woo Seong Chung
Monocular metric depth estimation (MMDE) is a crucial task to solve for indoor scene reconstruction on edge devices. Despite this importance, existing models are sensitive to facto…