3 ms·
It seems like building a context tree with a convex branch cross attention estimator then using branch and bound to prune the tree while descending to get exact
by CuriouslyC 3y ago
It seems like building a context tree with a convex branch cross attention estimator then using branch and bound to prune the tree while descending to get exact cross attention when it's above a threshold would work pretty well, assuming the cross attention matrix actually is very sparse and the trouble is just accurately guessing the non-sparse elements.
- zzzzzzzza 3y agothis sounds to me like a dollar cost averaging strategy - only buy in when the current price falls below an n-day moving average. I doubt there is any risk adjusted alpha to the strategy - in practice it's my, newbie, understanding that the only thing that differentiates such strategies in the broader scheme of things is tax efficiency. however I am also not a ML expert
- linuxdude314 3y agoWhat are you talking about? Wrong thread?
- zzzzzzzza 3y agoi am suggesting the two strategies might have similar trade offs/benefits though I am not familiar enough with attention mechanisms to say for sure. it's a comparison/analogy?