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Found 3 papers, 1 papers with code
Simplicity bias in the parameter-function map of deep neural networks
Deep neural networks can be viewed as a mapping from the space of parameters (the weights) to the space of functions (how inputs get transformed to outputs by the network).
Measuring the Volatility of the Political agenda in Public Opinion and News Media
Recent election surprises, regime changes, and political shocks indicate that political agendas have become more fast-moving and volatile.
Deep learning generalizes because the parameter-function map is biased towards simple functions
We then provide clear evidence for this strong simplicity bias in a model DNN for Boolean functions, as well as in much larger fully connected and convolutional networks applied to CIFAR10 and MNIST.
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