Jastrz ebski et al. [6] suggested that the ratio between the learning rate and the batch

Furthermore, many important constrained distributions are inherently non-log-concave.

While possessing high representational capacity, these architectures primarily operate on grid-like or sequential structures due to their built-in "innate priors".

Due to the non-convex nature of such joint estimators, the theoretical justification of their efficiency is typically challenging.

As a result, the brain needs to perform these computations in an approximate manner.

Wenwu Zhu is the corresponding author.

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Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer tissue. Therefore a group of graders typically produces a set of diverse but plausible segmentations.

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