How to pronounce“high dimensional”in English

a high dimensional control surface without feeling the thing physically?

very high-dimensional array of sense data and the model that the brain is running of the body has to be high dimensional because there's a lot going

That's a high dimensional data visualization program.

It's high dimensional, it's complex, we can't interpret it in its raw form.

with this high dimensional neural network and in observational causal Inference strong overlap becomes less plausible

so beta is a high dimensional vector.

Something like using high dimensional variables as control variables, using them for propensity scores, using high dimensional instruments.

Video is high dimensional and continuous.

you you define this high dimensional boundary which then gets captured in software to stay safe and actually do the right thing but uh it's kind of

well at the time high dimensional input um you know a couple dozen dimensions and turn that into a prediction as to oh is it going to be better if i stop at

And we want that's too high dimensional, too complicated to use directly.

Speaker 1: It's insanely high dimensional.

What gives you unconfoundedness is not too high dimensional.

But this linear model may be very high dimensional.

But if you're interested in the high dimensional setting, then these two papers are pleased to get started.

And that is simply a very high dimensional vector for which the logit that we have on the slide here, wouldn't work particularly well.

because it doesn't work in high dimension if your presentation is high dimensional there's just too many ways for two things to be different

described is kind of uh in these high dimensional spaces focus on the meeting place of uh two planes in high dimensional spaces

Speaker 2: So couldn't that be insanely high dimensional for each data, for example?

And you're absolutely right that if X was high dimensional, then strong overlap is implausible.

We don't want to just have a high dimensional linear prediction model.

However, in high dimensional data, and in many typical datasets that we may work with that have a very high number of covariates.

So it's quite difficult to take this really high dimensional, very deep structure, and project it into things that we really can understand

We do not know how to represent distributions over high dimensional continuous spaces in ways that are useful.

factors probably influencing your physical state your body very high dimensional yeah very high dimensional um

So I think if we thought that neural networks couldn't handle the insanely high dimensional data, then they would be useless in

And I said unstructured data are data that you know are too high dimensional, too complex, too uninterpretable to plug directly into your analysis.

- Yeah, I think life is very high dimensional.

graphs and build representations of this knowledge in these sort of very high dimensional spaces, being able to project them together jointly between, say,

and so every word is basically a set of coordinates in a high dimensional space and then they use some kind of trick to also encode the order of the words in a

What they're bad at is looking at high dimensional information.

It turns out that with the somatosensory system, you can take in high dimensional information, which is why you can balance on one leg.

Instead of staring at monitors, just imagine feeling the state of the factory in this high dimensional system.

But I'm talking about feeling how the whole system is going and where it needs-- where the pattern is moving in this high dimensional space.

but not in three dimensional space, but in some bigger high dimensional space of however many neurons."

And so we're assuming here that it's okay to feed high dimensional data into the neural so

And so in deep learning you don't tell the computer the rules that you're going to use to map that high dimensional data into

It wouldn't work particularly well first, because that vector x is far too high dimensional for it to work well.

And because it does so, it is actually able to work with very high dimensional data such as image data.

And because it's a multidimensional thing, the set of skills is a high dimensional space, you can't measure.

dimensional continuous space and we just have no idea how to do this properly uh finite high dimensional so like you because it's fine

the London Institute of Mathematical Sciences says, far from approaching AGI, AI has not progressed beyond high dimensional curve fitting.

There's information from all these different muscle groups coming in, and my brain has no problem integrating this high dimensional information to do that,

The answer probably is no in the sense that the way that you hear is this very high dimensional pattern.

So at this meeting, I was talking about a new project we had started where we were using medical imaging software on high dimensional astronomy data.

They try to show you when you have high dimensional data sets, a set a series of multiple two-dimensional

And so kind of the basis for all of this is like that you can feed high dimensional unstructured data into a neural network and

And so if you want to say that kind of in a technical way, we're going to take our high dimensional text.

And we wanted to fit a cate function of tau of X, where now tau of X is linear in X, but where X is high dimensional,