Speakers in the public record
Claim mix
evaluation 3prediction 2preference 2commitment 2uncertainty 1recommendation 1
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The useful parts, with receipts.
11 published records
“It's a CUDA library. And one of the things that, that we found frustrating is every time that we built one of these new architectures, and I'm sure you had the exact same experience, we'd have to go and spend two months in CUDA land, like writing these, these new efficient things.”
- Publisher
- Latent Space
“There's this NVIDIA and MIT put out this new diffusion model called SANA recently that one of their key key observations is that you can take a standard diffusion transformer diffusion model, replace the layers with linear [00:19:00] attention, and then that lets you scale to much larger much larger images, much, much Much larger sequences more efficiently. And and one thing that I don't think anybody would have called when a few years ago is that one of those gated SSM, gated states based models ended up on the cover of Science because a great group of folks went and trained some DNA models.”
- Publisher
- Latent Space
“If we did, if let's say we increased our brain size for every second we live, we would have exploded by the time we are 5 years old or something like that. And, and I think, I think basically fundamentally for us, right, be it whether we, regardless of whether RWKB, statespace, XLSTM, [00:35:00] etc, our general idea is that instead of that expanding state, that increase in computational cost, what if we have a fixed state size?”
- Publisher
- Latent Space
“Along those same lines, so afterwards we started putting out some work on specialized kernels, so just [00:12:00] like we have flash attention for transformers, we also have works like flash fft conf, and if you look at these lines of work oftentimes when, whenever you see a new architecture, you see a new primitive one of the, one of the table stakes now is, do you have an efficient kernel so that you can actually get wall clock speed up?”
- Publisher
- Latent Space
“You start to cascade your compute all the way until you are, Hey, I'm using 100 percent of the GPU. So we, we worked on it, and we started going along the principle of that as long as we keep this general architecture [00:24:00] where, where we can cascade and, and be highly efficient with our architecture, nothing is sacred in our architecture.”
- Publisher
- Latent Space
“It probably will work for Mamba as well, I don't see why not. And we will probably see more ideas, or more experiments, or more hybrids, or Yeah, like, one of the weirdest things that I wanted to say outright, and I confirmed this with the Black Mamba team and the Jamba team, which because we did the GoFinch hybrid model, is that none of us understand why a hard hybrid with a state based model to be R.”
- Publisher
- Latent Space
“rom here, if we take out this non linearity in the middle of the attention operation, and then if you compute the keys and the values operation first, you actually never hit this quadratic bottleneck.”
- Publisher
- Latent Space
“Because I think to understand architecture, right, it's probably easier to understand it from the RNN lens.”
- Publisher
- Latent Space
“Because [00:26:00] someone already paid that billion dollars, a million dollars onto training, so why don't we take advantage of those weights?”
- Publisher
- Latent Space
“I know I just gave a whole talk about it, but you know, what, what's the point of doing research if you can't, you know, play both sides. But I think one of the, so I think for both of us, the reason that we first got into this was just from the first principled questions of there's this quadratic thing.”
- Publisher
- Latent Space
“Yeah, I think that if I were to turn back the clock to 2022, that's probably one of the things I would have done differently, which would have been actually get some long context benchmarks out at the same time as we started pushing context length on all these models.”
- Publisher
- Latent Space