Neural Network Weight

Neural network weights, the parameters encoding a model's learned knowledge, are a central focus of current research aiming to improve model efficiency, interpretability, and generalization. Active areas include developing methods for compressing weights (e.g., through quantization, pruning, and exploiting symmetries), learning representations of weight spaces to facilitate model generation and transfer learning, and understanding the relationship between weight distributions and model performance. These advancements are crucial for deploying larger, more powerful models while addressing challenges like catastrophic forgetting and improving the efficiency of training and inference.