Gaussian processes with optimal kernel construction for neuro-degenerative clinical onset prediction
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The Nexlyze Insight on Instagram: "When neural networks get wide enough, they start behaving like probability models. As layers grow very wide, each neuron aggregates many independent signals. By the central limit effect, those sums approach a Gaussian distribution. In the infinite-width limit, a neural network can be viewed as a Gaussian Process — meaning its predictions are defined not by individual weights, but by a covariance (kernel) function that captures how outputs at different inputs re
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