Ying Sun

Structured pruning offers a hardware-friendly approach for efficient LLM inference. Early static methods determine fixed subnetworks through offline calibration, suffering from performance degradation and calibration sensitivity. Recent methods explore input-adaptive pruning by selecting a subset...

Representation Fine-tuning (ReFT), a recently proposed parameter-efficient fine-tuning (PeFT) method, significantly improves parameter efficiency by modifying the representation space alone. However, directly applying ReFT, which alters a fixed number of representations at the beginning and end p...

Large Language Models (LLMs) exhibit strong general reasoning but struggle in molecular science due to the lack of explicit priors required for precise chemical reasoning. Current solutions inject priors into parameters, but this static coupling hinders rapid knowledge updates and often compromis...

The emergence of generative models enables the creation of texts and images tailored to users' preferences. Existing personalized generative models have two critical limitations: lacking a dedicated paradigm for accurate preference modeling, and generating unimodal content despite real-world mult...

The rapid expansion of cryptocurrencies, especially Bitcoin, has heightened the need for efficient detection of illicit transactions, including ransomware payments and money laundering. To effectively address the unique challenges of Bitcoin fraud detection, such as handling heterogeneous, direct...