175 citations · 190 across the 5 of their papers we have counts for
5 papers
OckBench: Measuring the Efficiency of LLM Reasoning
Zheng Du, Hao Kang, Song Han +2
Large language models (LLMs) such as GPT-5 and Gemini 3 have pushed the frontier of automated reasoning and code generation. Yet current benchmarks emphasize accuracy and output qu…
SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
Samir Khaki, Xiuyu Li, Junxian Guo +7
Fine-tuning LLMs is both computationally and memory-intensive. While parameter-efficient fine-tuning methods, such as QLoRA and DoRA, reduce the number of trainable parameters and…
Wolf: Dense Video Captioning with a World Summarization Framework
Boyi Li, Ligeng Zhu, Ran Tian +20
We propose Wolf, a WOrLd summarization Framework for accurate video captioning. Wolf is an automated captioning framework that adopts a mixture-of-experts approach, leveraging comp…
Tiny Machine Learning: Progress and Futures
Ji Lin, Ligeng Zhu, Wei-Ming Chen +2
Tiny Machine Learning (TinyML) is a new frontier of machine learning. By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scop…
PockEngine: Sparse and Efficient Fine-tuning in a Pocket
Ligeng Zhu, Lanxiang Hu, Ji Lin +4
On-device learning and efficient fine-tuning enable continuous and privacy-preserving customization (e.g., locally fine-tuning large language models on personalized data). However,…