DeepSeek’s latest technical paper, co-authored by the firm’s founder and CEO Liang Wenfeng, has been cited as a potential game changer in developing artificial intelligence models, as it could translate into improvements in the fundamental architecture of machine learning.
The paper’s theme of Manifold-Constrained Hyper-Connections (mHC) marks an improvement to conventional hyper-connections in residual networks (ResNet), a fundamental mechanism underlying large language models (LLMs),…
Source: DeepSeek proposes shift in AI model development with ‘mHC’ architecture to upgrade ResNet
