Unlimiformer: Long-Range Transformers with Unlimited Length Input

Author(s): Reza Yazdanfar

Originally published on Towards AI.

Now it’s possible to have deep learning models with no limitation for the input size.

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Attention-based transformers have revolutionized the AI industry since 2017. Since then, we have seen significant progress in all aspects, including Computer vision, NLP, …

Attentions are considered a more powerful and capable version of Neural Networks for generalization on big datasets and are nothing more than routing between keys (K) and queries (Q), then non-linearity (Softmax), and then values (V).

Numerous variations of attention have been developed for various reasons, including optimizing complexity, decreasing computation, etc. One main branch is about transformers' capacity. How much can we give a model? How much can the model remember? …

My previous article was bout extending… Read the full blog for free on Medium.

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Published via Towards AI