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Surprisingly, even models as simple as LIF can capture the input-output relationship very well, despite the fact that only about 50% of the output spikes of the neuron are accurately predicted by the model.
That's the goal of Lobe, a startup and platform that genuinely seems to have made AI models as simple to put together as LEGO bricks.
A weighted k-nearest neighbor (kNN) approach was used for model fitting to make the models as simple as possible [59, 60].
Also, while building sophisticated models, it is important to keep the models as simple as possible, so that techniques based on these models are still practical.
We decided to use only 2D descriptors to keep the models as simple as possible, to speed up predictions, and to avoid repeatability problems associated with 3D descriptor values.
In line with the purpose of this study, we keep the models as simple as possible and abstract from concepts like volume change, turgor, transcription, etc. that are known to be involved in HOG signaling and osmo-adaptation [2].
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In this paper, First-Principle Data-Driven (FPDD) techniques have been considered in the modelling phase, in order to obtain a model as simple as possible but accurate enough.
They make great efforts to keep their business model as simple as possible.
But Holland says that the company wants to keep its pricing model as simple as possible.
This is because I would like to make the model as simple as possible.
To that end, we have kept the model as simple as possible.
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