Deeper Model

Deeper models, characterized by increased layers in neural networks like Transformers and Graph Transformers, aim to improve performance by capturing more complex relationships in data. Current research focuses on mitigating challenges associated with deeper models, such as rank collapse, oversmoothing, and increased computational costs, through techniques like attention masking, exponential decay, and parameter-efficient architectures including multi-path structures and knowledge distillation. These efforts are significant because they address the trade-off between model depth, performance, and resource consumption, impacting both the efficiency of training large models and their deployment in resource-constrained environments.