Enhancing Inference Efficiency and Accuracy in Low-Resource Tabular Code Generation via LapDDPM Sparsity Strategies

Abstract

Abstract: Generating high-fidelity and biologically plausible synthetic single-cell RNA sequencing (scRNA-seq) data, especially with conditional control, is challenging due to its high dimensionality, sparsity, and complex biological variations. Existing generative models often struggle to capture these unique characteristics and ensure robustness to structural noise in cellular networks. We introduce LapDDPM, a novel conditional Graph Diffusion Probabilistic Model for robust and high-fidelity scRNA-seq generation. LapDDPM uniquely integrates graph-based representations with a score-based diffusion mode

Research Question

Can the high-dimensional sparsity handling strategies from LapDDPM enhance inference efficiency and accuracy retention in low-resource tabular data benchmarks for code generation models?

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