Baseline Transformer

Baseline Transformer models serve as foundational architectures for various machine learning tasks, particularly in natural language processing and computer vision, but their computational demands often limit practical applications. Current research focuses on improving efficiency through techniques like attention mechanism optimization, novel positional encoding methods, and architectural modifications such as incorporating control theory principles or employing mixture-of-experts approaches. These efforts aim to reduce parameter counts, memory usage, and computational complexity while maintaining or improving performance, thereby broadening the applicability of Transformer models across diverse domains.