PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer

1. 1Understand the limitations of self-attention in transformers, particularly its quadratic complexity
2. 2Learn about the Polynomial Mixer (PoM) and its linear complexity
3. 3Implement PoM as a drop-in replacement for self-attention in existing transformer models
4. 4Evaluate the performance of PoM-equipped transformers on benchmark tasks

ML researchers and engineers working on transformer models can benefit from PoM as it provides a more efficient alternative to self-attention, allowing for faster training and inference times

💡 PoM achieves linear complexity while preserving the contextual mapping property, making it a promising replacement for self-attention

🚀 PoM: A faster alternative to self-attention in transformers! 🤖