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Having access to "infinite compute" is absolutely not a necessary factor for doing good research. In more ways than one, the opposite is truehttps://twitter.com/benhamner/status/966823205842243584 …
In machine learning, models that generalize are those with significant constraints -- regularization, limited memorization capacity, architecture encoding abstract assumptions about the data. The purpose of models is to generalize, not to optimize
In research, extensive hyperparameter tuning & architecture search results in ovefitting to a task (to the distribution of a specific dataset, even with no information leaks from the test set). But the goal of research is to generalize, not to optimize. To create new knowledge.
The trap of compute is the temptation to substitute research for optimization
Very true. La nécessité est la mère de l'invention. But there is also the (somewhat paradoxical) observation that doing good research is (partially) motivated toward bringing funding support for improving one's computing facilities ...
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