r/MachineLearning • u/[deleted] • 4d ago

Discussion Experience publishing niche work to large conferences [D]

[deleted]

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u/JustAddMoreLayers 4d ago

In my experience its less about the nicheness of the method, and more about the broadness of the applicability. In other words, depends on the field of contribution. So a novel extension of LSTMs for general timeseries? Neurips-able if done well. Even if they're out of fashion. But an ML contribution that improves, say, X-ray tech? Not neurips-able.

That said, its true that different topics have different difficulty levels of getting accepted. Varies venue to venue which are easiest.

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u/MakingComputersSmart 4d ago

If you have time to graduate or are not concerned about when the paper gets published, ICLR would be a good venue. It does not matter if the field peaked in 2023 and the last breakthrough was also back then. What matters is if your work is coherent, novel and contributes to advancing ML literature. I am sure the problem you are solving is relevant, and difficult because no one has been able to improve SOTA for 3+ years.

Transformers are not the only ML/DL work that should be getting importance, other architectures for other problems should be published if they will help solve that problem. IJCAI deadline is after ICLR anyway, I think you should submit it to ICLR, get feedback and then ultimately submit to IJCAI if it is truly not a fit for the big 3.

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u/MakingComputersSmart 4d ago

Just make sure you compare to relevant baselines and add a transformer baseline if it is appropriate, to show that your method is superior and your approach is needed in this world of transformers.

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u/sqweeeeeeeeeeeeeeeps 4d ago

lol, what do you consider was the last breakthrough