arXiv Machine Learning By Sahil Rajesh Dhayalkar

Kilobyte Models: Neural Networks as a Seed and a Quantized Latent

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arXiv:2608. 00860v1 Announce Type: new Abstract: The cost of storing and transmitting a trained neural network scales with its parameter count, a bottleneck for over-the-air updates, on-device libraries, and other bandwidth-bound deployments.

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arXiv AI
Sep 2

A Mathematical Theory of Reusable Neural Bases for Network Compression

The paper introduces the Linear Reusable Neural Bases Architecture (LRNBA), a framework that represents each network block as a linear combination of shared neural bases to improve parameter efficiency and reduce memory cost. Inspired by recurrent neural network designs, LRNBA enables the construction of wider and deeper networks within the same parameter budget. Experiments show that models using LRNBA converge as fast or faster than classical architectures, achieve lower loss, and maintain stable training dynamics.

By Binshuai Wang