Linear Reusable Neural Bases Architecture for Network Compression
Memory constraints remain a critical bottleneck in the deployment of large-scale AI models. Parameter sharing across network depth reduces model storage, but repeatedly applying an identical transformation limits flexibility across layers. Inspired by time--memory trade-offs in classical algorithms, we introduce the Linear Reusable Neural Bases (LRNB) architecture, an RNN-based framework that improves parameter efficiency through parameter reuse at the \textit{neuron level}. Each feedforward residual module is represented as a linear combination of shared neural bases, with depth-specific learnable coefficients and optional shifts providing flexibility across layers. This formulation redu...
| Authors | Binshuai Wang, Peng Wei, Mahyar Ghazanfari |
|---|---|
| Primary category | cs.LG |
| Published | 1 September 2026 |
| Last updated at source | 27 September 2026 |
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