"The importance of designing your Neural Networks right".pic.twitter.com/51ZDLcWEOL
People don't put enough effort into their baselines. You should use a solid library and do hyperparameter tuning. http://m-mitchell.com/NAACL-2016/SemEval/pdf/SemEval147.pdf …
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Many times I have come across papers showing baselines for LSTM that are below that the of the default Keras LSTM on the same dataset.
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good default hyperparameters, well-tested implementation.
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problem is reviewers don't always catch it. Weak baseline is almost always a negative review in my book, unless they have reasons.
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Question then: how about no baselines instead?
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