Summary of Know2vec: a Black-box Proxy For Neural Network Retrieval, by Zhuoyi Shang et al.
Know2Vec: A Black-Box Proxy for Neural Network Retrieval
by Zhuoyi Shang, Yanwei Liu, Jinxia Liu, Xiaoyan Gu, Ying Ding, Xiangyang Ji
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Know2Vec model retrieval scheme acts as a black-box proxy for model zoos, aiming to enable users to find suitable models without labor-intensive training. Current solutions struggle with inaccurate vectorization and biased correlation alignment, making it difficult to choose the best model. Know2Vec addresses this issue by capturing vital decision knowledge from models while ensuring their privacy, then employing an effective encoding technique to transform knowledge into precise vectors. The user’s query task is mapped to a knowledge vector through semantic relationships within samples. An optimized alignment space is established using supervised learning with diverse loss functions, allowing for accurate model retrieval during inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For general users, training a neural network from scratch can be difficult and time-consuming. Neural network zoos allow them to find well-performing models directly or fine-tune them in their local environments. The Know2Vec model retrieval scheme helps users choose the best model without labor-intensive training. It captures knowledge from models while keeping them private, then transforms it into precise vectors. This allows for accurate model selection based on a user’s query task. |
Keywords
» Artificial intelligence » Alignment » Inference » Neural network » Supervised » Vectorization
