Structurally Prune Anything (SPA)
A universal structured pruning framework for neural networks that works across any architecture, framework, and training stage
Posts by Collection
portfolio
Privacy-Preserving Soft Prompt Transfer (POST)
Efficient and privacy-preserving framework for transferring soft prompts between large language models with differential privacy guarantees
publications
Structurally Prune Anything: Any Architecture, Any Framework, Any Time
A versatile structured pruning framework that works across any neural network architecture, framework, and training stage, introducing OBSPA for post-training pruning without fine-tuning.
arXiv preprint ,
Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
A novel framework for transferring soft prompts between LLMs while preserving privacy through differential privacy and knowledge distillation techniques.
ICML 2025 ,
talks
Talk 1 on Relevant Topic in Your Field
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
UC San Francisco, Department of Testing ,
Tutorial 1 on Relevant Topic in Your Field
UC-Berkeley Institute for Testing Science ,
Talk 2 on Relevant Topic in Your Field
London School of Testing ,
Conference Proceeding talk 3 on Relevant Topic in Your Field
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Testing Institute of America 2014 Annual Conference ,
teaching
Machine Learning
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
University 1, Department ,