ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks

TL;DR

ParamReL introduces a novel framework that learns semantic representations directly from parameter spaces using a self-encoder, enhancing Bayesian Flow Networks' ability to model complex, mixed-typed data.

Contribution

It proposes a self-encoder-based method to learn high-level semantic representations from parameters, integrated into BFNs for improved data modeling.

Findings

Demonstrates effective learning of parameter semantics.

Enables conditional generation and reconstruction.

Shows superior performance in experiments.

Abstract

The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling parameter spaces, offering a unified strategy for handling continuous, discretized, and discrete data. However, BFNs cannot learn high-level semantic representation from the parameter space since {common encoders, which encode data into one static representation, cannot capture semantic changes in parameters.} This motivates a new direction: learning semantic representations hidden in the parameter spaces to characterize mixed-typed noisy data. {Accordingly, we propose a representation learning framework named ParamReL, which operates in the parameter space to obtain parameter-wise latent semantics that exhibit progressive structures. Specifically, ParamReL proposes a \emph{self-}encoder to learn latent semantics directly from parameters, rather than from observations. The encoder is then integrated into BFNs, enabling representation learning with various formats of observations. Mutual information terms further promote the disentanglement of latent semantics and capture meaningful semantics simultaneously.} We illustrate {conditional generation and reconstruction} in ParamReL via expanding BFNs, and extensive {quantitative} experimental results demonstrate the {superior effectiveness} of ParamReL in learning parameter representation.