LoMETab is a rank-r generalization of multiplicative implicit ensembles that strictly enlarges the hypothesis class for r >= 2 and supplies tunable control over predictive diversity via adapter rank and initialization scale.
HiRA: Parameter-efficient hadamard high-rank adaptation for large language models
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LoMETab: Beyond Rank-1 Ensembles for Tabular Deep Learning
LoMETab is a rank-r generalization of multiplicative implicit ensembles that strictly enlarges the hypothesis class for r >= 2 and supplies tunable control over predictive diversity via adapter rank and initialization scale.