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This short post is on the importance of having proper initialization while using alternating minimization for two variables, such that the objective function under consideration is convex in each variable individually but not jointly convex with respect to both the variables.
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This short post contains a few references for the relatively unknown (in my opinion) EVT among people who aren’t statisticians but use it heavily (such as in machine learning).
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This post contains a summary of recent advances in non-convex optimization in deep learning, discussing about the optimality of local minima for several models, the issue of saddle points and modifications to stochastic gradient descent which are robust to saddle points.
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This post contains some guidelines (gathered from self experience and also from some highly experienced people) for doing theoretical research in deep learning (and machine learning in general), strictly for newbies!