Adam Hibble @Algomancer
I generate models that generate other stuff, working on @mancerlabs -- Prev: Founder of Popgun Labs (Techstars), Founder of the @QUTCode Network. Joined August 2013-
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Fun and useful (for a change) - Wiped up a Closed-form JVP ConvNeXt-V2 U-Net. (FCDM style) Need this for some weird regularisation experiments, figured it might be useful to someone else. Probably useful for mean flows etc. Or when you need a volume change surrogate like i did. Has the conditioning tangent, so you can do all your time dependent jacobivibes. Closed-form JVP, so the whole encoder/decoder tangent is plain differentiable ops Two reasons this exists rather than a forward-AD dual_level pass over the plain net. It is compilable: torch.compile fuses jvp to ~plain-forward cost, versus the ~3-7x of the forward-AD dual. It is reverse-over-forward correct: Seems like on certain version of pytorch stock F.layer_norm's forward-AD rule gives a wrong second derivative. (the sign sometimes changes? Haven't investigated, but this wasted a lot of of compute). github.com/Algomancer/con…
Are you still working on gauss reg stuff? If so, would be keen for you to try my manifold constrained optimiser i've be tinkering on. Replaces your optimizer on your encoder. I've compared it to sigreg on a few formulations. Reformulates gaussianity as a constraint on the solution space, rather than as a regulariser.
Not a lawyer-nerd. If a frontier model company, materially changes a prompt a user provided are they accepting exposure proportionate to the causal contribution and any duty assumed? Especially if you've voluntarily undertaken to do that, and the voluntary-undertaking doctrine, especially if its marketed for safety, since intervening to reduce harm is exactly what creates the assumed duty? Seems like, intuitively at least, modify users input is a liability landmine.
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of
I still feel this way, 8 years later.
People sometimes ask if I think it's risky for everyone to have access to AI. I think it's MORE risky for an exclusive & homogeneous group alone to develop tech that impacts us all.
@rosmine Nah, your good. It's just shit.
I really don't like the feeling of anger man. It sucks. Im really disappointed by anthropic. I've been writing model code since 2010, everything I do looks like 'frontier research'. Like genuinely. The whole day has felt so shitty.
@rosmine I seriously don't like the feeling man. Not even enough to meme about it. This might be sufficient to shape how and what i work on and with who.
@ylecun @klindt_david @randall_balestr I really love the realnvp mixing test. But I will say I got unreasonably excited and then disappointed when I got the notification for this paper since the llm summary made it sound like it was a flow jepa. github.com/Algomancer/flo…
This is cool because you could use flows with expensive inverses like MAFs since you’re throwing them away after training
I have been thinking about the measure of how far away a transform moves an input from its 'automorphism group'. Intuitively, this would seem to specialise (in expectation) to negentropy / expected log-det Jacobian. vaguly, it'd result in flows that act more as a gaussanity tests/regulariser, where the 'goal' is to starve the flow, ie isometry log-det -> 0, singular values -> 1
I don’t know the upper bounds but i have comfortably handled 16 nodes solo, more of a budget limitations - but I understand I’m more comfortable with operations than most so I think I’m mostly going to tap out on gpu faults. Imagine anything beyond I’d probably need to think more about topology, but assuming no physical faults I can usually figure it out. I will say tho I’m perpetually compute poor so I’d hedge beyond that. and tend to work entirely solo so don’t have much ‘other people overhead’ wrt to job scheduling. Pref just to have root.
gonna revisit this, i really liked the split head formulation for compute reuse.
A Ladder of Heads Varient of Free Transformer Each transformer layer contains a binary VAE: where the "Encoder" and "Decoder" live inside the same Transformer layers, separated only by attention masks and head splits. Decently efficient thanks to flex attention. First N heads:
artifact from a discord chat wake-sleep Bayesian program learning in one PyTorch file. Integer arithmetic domain. Joint inference over a typed contextual PCFG and a neural recognition model that amortizes the posterior over programs given I/O examples. basically a nano dream coder github.com/Algomancer/bay…
me: pull out this into a single .py for github claude: got it im changing all your numerics for no reason
quietly good paper for anyone training discrete latent variable models. lower variance gradients for discrete latents, with a nice reframing of the whole estimator family. this one rao-blackwellizes the noisy bit and patches the leftover bias with a control variate. Did a quick implimentation here and a mapper layer for vaes like free transformer. github.com/Algomancer/Bet…
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