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Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels
We propose a new method for approximating active learning acquisition strategies that are based on retraining with hypothetically-labeled candidate data points.
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
Empirical neural tangent kernels (eNTKs) can provide a good understanding of a given network's representation: they are often far less expensive to compute and applicable more broadly than infinite width NTKs.
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