Summary of Paramrel: Learning Parameter Space Representation Via Progressively Encoding Bayesian Flow Networks, by Zhangkai Wu et al.
ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks
by Zhangkai Wu, Xuhui Fan, Jin Li, Zhilin Zhao, Hui Chen, Longbing Cao
First submitted to arxiv on: 24 May 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 Bayesian Flow Networks (BFNs) offer a unified strategy for handling continuous, discretized, and discrete data. However, they cannot learn high-level semantic representations from the parameter space since common encoders fail to capture semantic changes in parameters. To address this limitation, we introduce ParamReL, a representation learning framework that operates in the parameter space to obtain latent semantics with progressive structures. ParamReL proposes a self-encoder to learn latent semantics directly from parameters, rather than observations. We integrate this encoder into BFNs, enabling representation learning with various formats of observations. Mutual information terms promote the disentanglement of latent semantics and capture meaningful semantics simultaneously. Our experiments demonstrate the superior effectiveness of ParamReL in learning parameter representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ParamReL is a new way to learn about data by looking at the numbers that describe it. Right now, we can’t easily see what these numbers mean or how they relate to each other. This makes it hard to work with noisy data that has different types of information mixed together. ParamReL helps solve this problem by creating a system that learns about these numbers and finds patterns within them. It does this by looking at the parameters that describe the data, rather than just looking at the data itself. This allows us to learn more about the data and make better decisions based on it. |
Keywords
» Artificial intelligence » Encoder » Representation learning » Semantics
