Weight Matrix

Weight matrices, the core components of neural networks, are the subject of intense research focused on improving efficiency, generalization, and interpretability. Current efforts explore low-rank approximations, structured matrices (e.g., Monarch, Block Tensor-Train), and novel training methods like weight decay and parameter-efficient fine-tuning (PEFT) techniques such as LoRA, to optimize their structure and reduce computational costs. These advancements are crucial for scaling up deep learning models, enabling their application to larger datasets and more complex tasks, and enhancing our understanding of how these models learn and generalize.