Sparse-by-Design Cross-Modality Prediction: L0-Gated Representations for Reliable and Efficient Learning
Predictive systems increasingly span heterogeneous modalities such as graphs, language, and tabular records, but sparsity and efficiency remain modality-specific (graph edge or neighborhood sparsification, Transformer head or layer pruning, and separate tabular feature-selection pipelines). This fragmentation makes results hard to compare, complicates deployment, and weakens reliability analysis across end-to-end KDD pipelines. A unified sparsification primitive would make accuracy-efficiency trade-offs comparable across modalities and enable controlled reliability analysis under representation compression. We ask whether a single representation-level mechanism can yield comparable accura...
| Authors | Filippo Cenacchi |
|---|---|
| Primary category | cs.LG |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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