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If you train a neural network you get some results. But presumably if you train a 1000 separate neural networks on different measurements made of the same targe
by galaxyLogic 6y ago
If you train a neural network you get some results. But presumably if you train a 1000 separate neural networks on different measurements made of the same target, and then let those thousand models vote, isn't that a new kind of learning mechanism, compared to having a single neural network that always takes in all the inputs?
Further each of the thousand neural nets could have a different learning algorithm.
- lostmsu 6y ago> let those thousand models vote Soon after you start thinking how voting can be implemented, you will quickly realize, that one of the best ways to do it is to connect each of 1000 networks to each of N outputs with some sort of weights (in trivial version - uniform), which effectively turns 1000 networks into 1 network with extra N unit layer, and no weight reuse (e.g. probably a disadvantage) in all but the last layer.