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Found 2 papers, 1 papers with code
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias
Recent work by Dai and Wipf (2020) proposes a two-stage training algorithm for VAEs, based on a conjecture that in standard VAE training the generator will converge to a solution with 0 variance which is correctly supported on the ground truth manifold.
Unsupervised Program Synthesis for Images By Sampling Without Replacement
However, they struggle with the inherent sparsity of meaningful programs in the search space.
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