one subtle consistency bug that made it hard for me to interpret when I was clicking around: the small thumbnail plot vs the full plot often (always?) seem to use different colors.
The blue / orange gets assigned to the opposite labels in the A vs. B when you click, which made it confusing to understand.
One upside of the deterministic schemes is they include provenance/lineage. Can literally "trace up" the path the history back to the original ID giver.
Kinda has me curious about how much information is required to represent any arbitrary provenance tree/graph on a network of N-nodes/objects (entirely via the self-described ID)?
(thinking in the comment: I guess if worst case linear chain, and you assume that the information of the full provenance should be accessible by the id, that scales as O(N x id_size), so its quite bad. But, assuming "best case" (that any node is expected to be log(N) steps from root, depth of log(N)) feels like global_id_size = log(N) x local_id_size is roughly the optimal limit? so effectively the size of the global_id grows as log(N)^2? Would that mean: from the 399 bit number, with lineage, would be a lower limit for a global_id_size be like (400 bit)^2 ~= 20 kB (because of carrying the ordered-local-id provenance information, and not relative to local shared knowledge)
I almost feel like this goes opposite to what attention is good at. This would be good at approximating all the places where attention is low / not sharp. Where attention/the exponential is key is when it selects out / needle-in-haystack / winner-takes-all focus (the word "attention" itself sorta implies this), and this is where the taylor expression would fail to represent the values well. This just... softens attentions ability to attend?
(I'm imagining that if in the context there's ~4-8 "similar" attention-targets that should be sharp, and regular attention learns to select the correct one, this taylor approximation version would wash out any difference and they'd all loosly be attended to, and it'd fail to isolate the correct signal)
Really wish this had some downstream tests -- apply it to a pretrained model and see how performance degrades, train a fresh one, etc. The tests are worth doing, but I somehow don't feel that hopeful this is the unlock required for sub-quadratic attention. It's possible that a freshly trained model with this learns to attend without the sharp attention signals, but that seems a bit dubious to me.
But also, maybe this combined with some other selective (sparse attention) trick, means that the hybrid model gets the "fuzzy long tail" of attention well represented as well as the sharpness well represented, and all together it could actually be a part of the larger solution.
Previous paper from DeepSeek has mentioned Anna’s Archive.
> We cleaned 860K English and 180K Chinese e-books from Anna’s Archive (Anna’s Archive, 2024) alongside millions of K-12 education exam questions.
https://arxiv.org/abs/2403.05525
DeepSeek-VL paper
The first time I got off at and heard Komagome's tune I mistakenly thought it was some halloween special because it was late October at the time, and the song felt so distinct and unique.
Interestingly this one seems it is from before 高輪ゲートウェイ (Takanawa Gateway) station which opened in 2020, but the numbering shows the gap (JY 25 -> JY 27). That led me to looking it up, and turns out that they introduced the numbering in 2016, and that already came pre-planned with the gap ready [1].
I’ve been using whisky to play Elden ring on my M4 MBP and it’s been great! I love that the Game porting toolkit and wine all work so well.
I did have to do some pinning of steam to an older version to keep it working recently. I guess I’ll move over to crossover soon
one subtle consistency bug that made it hard for me to interpret when I was clicking around: the small thumbnail plot vs the full plot often (always?) seem to use different colors.
The blue / orange gets assigned to the opposite labels in the A vs. B when you click, which made it confusing to understand.