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In gradient descent, changing how we parametrize the model can lead to drastically different optimization trajectories, giving rise to a surprising range of meaningful inductive biases: identifying sparse classifiers or reconstructing low-rank matric
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6853a2d1e57b1ef3b13d66b392800ab5
http://arxiv.org/abs/2111.11542
http://arxiv.org/abs/2111.11542
