Explicit Weight

Explicit weight assignment in neural networks is a crucial area of research focusing on how to optimally determine the connection strengths between neurons. Current efforts explore novel weight constructions for state-space models and large language models (LLMs), aiming to improve prediction accuracy and efficiency through techniques like tensor decomposition and targeted weight adjustments. Understanding the role of negative weights in achieving universal approximation and the development of simpler, more robust algorithms like tangential LLE are also key themes, impacting both theoretical understanding of network capabilities and the practical development of more efficient and interpretable AI systems. These advancements contribute to a deeper understanding of neural network function and pave the way for more effective and resource-conscious AI.