![]() We explore the interaction between our method and the choice of normalization layer, and demonstrate its robustness across a variety of architectures and training sets. However, an adverse effect already observed for sparsely permuted data is that the least squares estimator as well as other estimators not accounting for such. Second, by combining this with an algorithm for finding permutations based on maximizing correlations between the activations of matched neurons, we are able to reduce the interpolation barrier for a standard ResNet18 trained on CIFAR-10 to 1.5% absolute test error. ![]() We demonstrate that an appropriate rescaling of the pre-activations of the interpolated networks ameliorates this problem and significantly reduces the barrier. First, we observe a general phenomenon in which interpolated deep networks suffer a collapse in the variance of their activations. As if Margulis’s proposal for nucleated cells was not radical enough, the concept of groups permuting into organisms was generalized by two theoretical biologists, John Maynard Smith and Eors Szathmary in the 1990s. ![]() However, the order of the subset matters. We conduct our investigation using standard computer vision architectures trained on CIFAR-10 and ImageNet. Like the Combinations Calculator the Permutations Calculator finds the number of subsets that can be taken from a larger set. It assumes that data are randomly selected from the population, arrived in large samples (>30), or normally distributed with equal variances between groups. To compare outcomes in experiments, we often use Student’s t-test. (2021) which states that if permutation invariance is taken into account, then there should be no loss barrier to the linear interpolation between SGD solutions. Photo by Eric Prouzet on Unsplash Introduction. Abstract: In this paper we empirically investigate the conjecture from Entezari et al. ![]()
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