A Unified Theoretical Framework for Wide Neural Network Learning Dynamics

View a PDF file from the paper entitled Connecting NTK and NNGP: A unified theoretical framework for extensive nerve network learning dynamics, by Yahionan Avidan and 2 other authors

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a summary:Synthetic nervous networks have revolutionized machine learning in recent years, but the complete theoretical framework of the learning process is still not present. Great developments of wide networks have been achieved, within two different theoretical frameworks: nervous nucleus (NTK), which assumes the dynamics of linear gradients, and the NNGP nervous network. We unify these two theories using gradient lineage learning with additional noise in a range of wide deep networks. We build an analytical theory of the intervention of the network and outputs and the introduction of a nerve -based dynamic nucleus nucleus (NDK) through which both NTK and NNGP nucleus are derived. We define two educational phases: an educational stage that depends on gradient, dominated by reducing the loss, as the time scale is subject to a change of preparation. This is followed by a slow educational stage, where the teachers try the solution space, with the time settled by the previous noise and contrast. My teacher has strongly affecting performance in the two systems, especially in X -ray neurons. In contrast to the si convergence of the average forecaster in the initial stage, the rapprochement with balance is more complicated and may act as bad. By describing the prevailing stage, our work sheds light on the acting erosion in the brain, and explains how the neurological activity changes continuously without the deterioration of performance, either through the continuous gradual signals that are synchronized by the drifts from different nerve clashes or through architectural biases that generate related information to the task that are strong against the slope process. This work closes the gap between the theories of NTK and NNGP, providing a comprehensive framework for the learning process for deep deep nervous networks and dynamic analysis in biological circuits.