Title: Exploring State-Space Models for Data-Specific Neural Representations
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: state-space model

Abstract: This paper studies the problem of data-specific neural representations, aiming for compact, flexible, and modality-agnostic storage of individual visual data using neural networks. Our approach considers a visual datum as a set of discrete observations of an underlying continuous signal, thus requiring models capable of capturing the inherent structure of the signal. For this purpose, we investigate state-space models (SSMs), which are well-suited for modeling latent signal dynamics. We first explore the appealing properties of SSMs for data-specific neural representation and then present a novel framework that integrates SSMs into the representation pipeline. The proposed framework achieved compact representations and strong reconstruction performance across a range of visual data formats, suggesting the potential of SSMs for data-specific neural representations.

TL;DR: This paper first applies the compressive property of state-space models to data-specific neural representations, which aim to represent a datum by overfitting a neural network.