A nonconvex l1/2-regularized nonnegative matrix factorization method with ADMM solver and detection estimation improves sparse network recovery under imperfect observations compared to baselines.
Matrix factorization techniques for recommender systems
3 Pith papers cite this work. Polarity classification is still indexing.
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FlowAdam adds clipped ODE integration to Adam with soft momentum injection for implicit regularization, cutting held-out error 10-22% on coupled matrix/tensor tasks while matching Adam on standard workloads.
An overview revisits LoRA variants by categorizing advances in architectural design, efficient optimization, and applications while linking them to classical signal processing tools for principled fine-tuning.
citing papers explorer
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Sparse Network Inference under Imperfect Detection and its Application to Ecological Networks
A nonconvex l1/2-regularized nonnegative matrix factorization method with ADMM solver and detection estimation improves sparse network recovery under imperfect observations compared to baselines.
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FlowAdam: Implicit Regularization via Geometry-Aware Soft Momentum Injection
FlowAdam adds clipped ODE integration to Adam with soft momentum injection for implicit regularization, cutting held-out error 10-22% on coupled matrix/tensor tasks while matching Adam on standard workloads.
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Low-Rank Adaptation Redux for Large Models
An overview revisits LoRA variants by categorizing advances in architectural design, efficient optimization, and applications while linking them to classical signal processing tools for principled fine-tuning.