The Sequence Scope: Simpler, More Efficient Transformers

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📝 Editorial: Simpler, More Efficient Transformers

It is hard to argue that transformers have become the most relevant architectures in modern machine learning (ML). Since the publication of the now-iconic Attention is All You Need paper, transformers have revolutionized fields like natural language understanding and computer vision. Now they are becoming highly relevant across most ML domains. However, transformer models require incredibly sophisticated ML infrastructures and remain too complex to be applied in many domains. If transformers hope to achieve mainstream adoption, they need to get simpler, more efficient and easier to operate.

The simplification transformers present challenges on both the ML research and engineering fronts. The good news is that the AI research community very well understands these challenges. Just this week, AI labs at Amazon Research and Stanford University published three papers about simplifying the training and efficiency of transformer architectures. Similarly…