Perhaps the above comment can provide an occasion for a useful interchange on the relation of DL to other approaches. I'll try: Last year a student of one of

Perhaps the above comment can provide an occasion for a useful interchange on the relation of DL to other approaches. I'll try: Last year a student of one of the authors credited in the video performed an interesting data modeling/structure discovery analysis for me. The problem had ~100 variables and ~300 observations. We used a latent-variable + sparse-interaction model and solved the resulting minimization problem with a convex optimizer, as described in the video. This approach was preferable to a DL technique for several reasons: we wanted to preserve some interpretability of the discovered latent variables (just 2 or 3 for our problem); we had reason to believe the problem had a sparse-linear structure because of conservation of mass; we didn't have much data relative to the number of variables. I don't see DL approaches (despite their many successes) as applicable to this kind of problem. The OP motivates his approach with recommender systems, where there are millions of outcome variables (customers), and thousands-to-millions of stimulus variables (products). That would result in a "lot" (considerable understatement) of connections for a DL model to learn. Unless I'm missing something, the hyper-scale recommender setting is also not the best place for a conventional multi-layer DL model -- too many connections. On the other hand, the explicit sparsity control built in to the OP's optimization is really helpful.