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Found 2 papers, 1 papers with code
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport
The energy landscape of high-dimensional non-convex optimization problems is crucial to understanding the effectiveness of modern deep neural network architectures.
A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis
In this paper, we generalize the SLTH to functions that preserve the action of the group $G$ -- i. e. $G$-equivariant network -- and prove, with high probability, that one can approximate any $G$-equivariant network of fixed width and depth by pruning a randomly initialized overparametrized $G$-equivariant network to a $G$-equivariant subnetwork.
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