Yi Zhan

Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models

2025 · arXiv (Cornell University)

Differential Privacy (DP) is a widely adopted technique, valued for its effectiveness in protecting the privacy of task-specific datasets, making it a critical tool for large language models. However, its effectiveness in Multimodal Large Language …

Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward

2025 · arXiv (Cornell University)

Reinforcement learning (RL) has been widely adopted to enhance the performance of large language models (LLMs) on Text-to-SQL tasks. However, existing methods often rely on execution-based or LLM-based Bradley-Terry reward models. The former suffers from …

SSKD: Stepwise Self-Knowledge Distillation for Binary Neural Networks in Keyword Spotting

2026 · Applied Sciences

The hardware power-aware keyword spotting (KWS) implementation requires small memory footprint, low-complex computation, and high accuracy performances. Binary neural networks (BNNs) naturally satisfy these constraints. They quantize both weights and activations to 1-bit. This reduces …

C-DPSS: Channel dual-phase sparsity pruning framework for spiking neural networks

2026 · Neurocomputing

Spiking Neural Networks (SNNs) have emerged as an essential paradigm for brain-inspired computing, achieving superior energy efficiency on neuromorphic hardware. However, as network scale increases, SNNs encounter growing challenges in deployment efficiency. While structured pruning …