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Research questionHow can neural networks retain capacity while fitting within fixed parameter and memory budgets?Large models consume substantial memory through their parameters, restricting the depth and width that can be trained or deployed within a given budget. Reducing this cost must not undermine optimization stability or predictive performance.
AI
AI Memory
Inference Optimization
Machine Learning
Neural and Evolutionary Computing
Technology
Latest papersRecent research connected to this question, newest first.A Mathematical Theory of Reusable Neural Bases for Network CompressionThe source concerns a reusable neural-bases architecture evaluated for parameter compression, convergence, loss, and training stability. It provides experimental evidence for large neural networks but does not specify particular model families, datasets, or deployment hardware.research paper · Sep 2, 2026
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