Researchers Devise a Method for Permuting Model Units Whose Transformation Yields a Functionally Comparable Set of Weights in an Approximately Convex Basin Around the Reference Model

Deep learning’s success is due to its capacity to tackle some enormous non-convex optimization problems with relative simplicity. Even though non-convex optimization is NP-hard, simple algorithms and generic versions of stochastic gradient descent perform surprisingly well in fitting massive neural networks in reality. After accounting for all conceivable permutation symmetries of hidden units, they conclude […]

The post Researchers Devise a Method for Permuting Model Units Whose Transformation Yields a Functionally Comparable Set of Weights in an Approximately Convex Basin …

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