1 citations · 1 across the 5 of their papers we have counts for
7 papers
SDFP: Speculative Decoding with FIT-Pruned Models for Training-Free and Plug-and-Play LLM Acceleration
Hanyu Wei, Zunhai Su, Peng Lu +4
Large language models (LLMs) underpin interactive multimedia applications such as captioning, retrieval, recommendation, and creative content generation, yet their autoregressive d…
UniGeM: Unifying Data Mixing and Selection via Geometric Exploration and Mining
Changhao Wang, Yunfei Yu, Xinhao Yao +5
The scaling of Large Language Models (LLMs) is increasingly limited by data quality. Most methods handle data mixing and sample selection separately, which can break the structure…
Seed-Coder: Let the Code Model Curate Data for Itself
ByteDance Seed, Yuyu Zhang, Jing Su +24
Code data in large language model (LLM) pretraining is recognized crucial not only for code-related tasks but also for enhancing general intelligence of LLMs. Current open-source L…
Group Relative Knowledge Distillation: Learning from Teacher's Relational Inductive Bias
Chao Li, Changhua Zhou, Jia Chen
Knowledge distillation typically transfers knowledge from a teacher model to a student model by minimizing differences between their output distributions. However, existing distill…
Controllable Segmentation-Based Text-Guided Style Editing
Jingwen Li, Aravind Chandrasekar, Mariana Rocha +2
We present a novel approach for controllable, region-specific style editing driven by textual prompts. Building upon the state-space style alignment framework introduced by \emph{S…
Text-Driven Video Style Transfer with State-Space Models: Extending StyleMamba for Temporal Coherence
Chao Li, Minsu Park, Cristina Rossi +1
StyleMamba has recently demonstrated efficient text-driven image style transfer by leveraging state-space models (SSMs) and masked directional losses. In this paper, we extend the…