Deep Ensembles: Why Random Starts Help Models Find Better Answers
Ever wonder why running the same model a few times makes it work better? Combining several runs — the so-called deep ensembles — often brings more accurate results and handles weird new data much better.
You don't need fancy tricks; just different seeds or random initialization pushes training into different parts of the problem, and that matters.
Some fancy methods try to cover many possibilities but they tends to stay near one answer, so they miss out.
By looking at the shape of the learning problem, the loss landscape, researchers checked how similar the model's outputs really are.
They found random starts make models choose very different ways to predict, creating diverse modes, while other sampling tricks keep predictions too close.
That mix of choices helps the system say when it is unsure and be more reliable under surprise data, improving model uncertainty.
Short story: simple randomness decorrelates models in a useful way, and often beats more complex sampling schemes, so using it is cheap and powerful.
Read article comprehensive review in Paperium.net:
Deep Ensembles: A Loss Landscape Perspective
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