Muon optimizer outperforms AdamW across 17 tabular datasets when training MLPs under a shared protocol.
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Pith papers citing it
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2026 4representative citing papers
LEMUR accelerates multi-vector retrieval by learning a neural network approximation to MaxSim and reducing it to single-vector search in latent space.
Lightweight pooling-aware fine-tuning with k-means on a single dataset enables up to 83% vector compression in ColBERT models with no retrieval accuracy loss and positive cross-dataset transfer.
SoftSignum replaces hard sign with soft-sign in optimizers via temperature control and quantile scheduling, extends to SoftMuon, provides a convergence proof for stochastic non-convex settings, and reports better performance than sign-based methods and AdamW on deep learning tasks.
citing papers explorer
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Benchmarking Optimizers for MLPs in Tabular Deep Learning
Muon optimizer outperforms AdamW across 17 tabular datasets when training MLPs under a shared protocol.
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LEMUR: Learned Multi-Vector Retrieval
LEMUR accelerates multi-vector retrieval by learning a neural network approximation to MaxSim and reducing it to single-vector search in latent space.
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Learn to Pool: Lightweight Fine-Tuning for Flexible Multi-Vector Compression
Lightweight pooling-aware fine-tuning with k-means on a single dataset enables up to 83% vector compression in ColBERT models with no retrieval accuracy loss and positive cross-dataset transfer.
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Softsign: Smooth Sign in Your Optimizer For Better Parameter Heterogeneity Handling
SoftSignum replaces hard sign with soft-sign in optimizers via temperature control and quantile scheduling, extends to SoftMuon, provides a convergence proof for stochastic non-convex settings, and reports better performance than sign-based methods and AdamW on deep learning tasks.