ShuffleGate learns polarized importance gates by measuring model sensitivity to random component shuffling, unifying feature selection, dimension selection, and embedding compression with SOTA results on four recommendation benchmarks.
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ShuffleGate: A Unified Gating Mechanism for Feature Selection, Model Compression, and Importance Estimation
ShuffleGate learns polarized importance gates by measuring model sensitivity to random component shuffling, unifying feature selection, dimension selection, and embedding compression with SOTA results on four recommendation benchmarks.