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Everybody knows how successful transformers have been in NLP. Is there known work on other domains (e.g that also have a sequential natural way of occurring, such as stock price prediction or other problems)?

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When it talks to other domains such as image or music, using transformer will always face a problem of sequence length limitation. To the best of my knowledge, the bottleneck of self-attention which uses a $n^2$ matrix quite limits transformer being applied to other domains. For example, a 32x32 pixel image, means a sequence of 1024 tokens.

OpenAI did some related research, as the followings.

Generative Modeling with Sparse Transformers: In the paper, transformers with sparse attention are applied to image and waveform.

ImageGPT: A large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples. (Abstract from the blog)

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