I believe that exploration of this kind is rather pertinent for AI alignment. It appears very plausible that neural networks, or a little something much like them, will be utilized to be a ingredient of AGI. If that is the case, then we wish to be able to reliably forecast and cause regarding how neural networks behave in new predicaments, And exactly how they interact with other systems, and it is difficult to assume how that could be probable without a deep idea of the dynamics at play when neural networks learn from knowledge.
On the other hand, in high dimensional options, most of the quantity is near the boundary, so this is simply not a major offer. I'm not aware of any work that claims SGD uniformly samples from this boundary, nonetheless it's worthy of considering that risk If your experimental results delay.
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OK, many thanks! The 1st bullet place is I think a summary of my initially two bullet factors, but with different emphasis. I'll check out the experiments you joined.
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determine 5a displays that SGD ordinarily chooses capabilities with Bayesian posterior earlier mentioned ~ten^-10 (keep in mind the x-axis is really a cutoff here), and capabilities with increased Bayesian posterior are usually picked out more normally by SGD
So it would not make sense to claim that two capabilities are equivalent (in how your talking about at the least), because they always vary on some input (or they'd be a similar perform). However, two packages is usually equivalent in they output a similar things (they compute equivalent features).
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