6 papers
Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed
Haokun Lin, Kaijie Zhu, Haobo Xu +4
Small Language Models (SLMs) have emerged as a more efficient alternative to traditional Large Language Models (LLMs), offering promising potential in resource-constrained scenario…
MAC-Splat: Multi-Attribute Consistency for High-Fidelity Sparse-View Reconstruction
Jinqian Yang, Yichen Wu, Wanhua Li +4
Reconstructing high-fidelity 3D scenes from sparse-views remains a central problem in generalizable neural rendering. Existing generalizable 3D Gaussian Splatting (3DGS) methods of…
DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization
Haokun Lin, Xinle Jia, Haobo Xu +7
The MXFP4 microscaling format, which partitions tensors into blocks of 32 elements sharing an E8M0 scaling factor, has emerged as a promising substrate for efficient LLM inference,…
Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs
Haokun Lin, Haobo Xu, Yichen Wu +6
Recent advances in diffusion large language models (dLLMs) have introduced a promising alternative to autoregressive (AR) LLMs for natural language generation tasks, leveraging ful…
LRQ-DiT: Log-Rotation Post-Training Quantization of Diffusion Transformers for Image and Video Generation
Lianwei Yang, Haokun Lin, Tianchen Zhao +6
Diffusion Transformers (DiTs) have achieved impressive performance in text-to-image and text-to-video generation. However, their high computational cost and large parameter sizes p…
Singular Value Fine-tuning for Few-Shot Class-Incremental Learning
Zhiwu Wang, Yichen Wu, Renzhen Wang +4
Class-Incremental Learning (CIL) aims to prevent catastrophic forgetting of previously learned classes while sequentially incorporating new ones. The more challenging Few-shot CIL…