Carlo!

Monte Carlo

Monte Carlo

Monte Carlo. Constantinopolee.

Carlo Marosi. This must be his.

Let's get back to Monte Carlo localization.

Monte Carlo techniques gives us an approximation.

Carlo would love to ask Carrie out.

That can be tricky here at Monte Carlo.

That priest you whacked was Carlo Ricci's brother.

Drop us off at Avenue Monte Carlo please.

He had the stupidity to try to help Carlo.

I'll get fifteen years. His name was Carlo Marosi.

My name is Carlo. Some of you know me already.

It uses a method called Markov Chain Monte Carlo, or MCMC.

There's very little room to pass on these narrow Monte Carlo streets.

In Monte Carlo localization, we don't predict the exact outcome. We add noise.

I told my parents about my sister going all the way with Carlo.

Is Jesse still driving the '89 Chevy Monte Carlo, as far as you know?

This software carries out Markov Chain Monte Carlo calculations by the use of Gibbs Sampling.

You learned about Monte Carlo robot localization with a technique that I often call histogram filters.

Here is a very early example of structure from motion by Carlo Tomasi and Takeo Kanade.

The Monte Carlo at the scene belonged to a former student of his a Jesse Pinkman.

Spent the next two days in Monte Carlo wondering why she let me go. Then she came for me.

Twice world champion, an absolute master of these twisting Monte Carlo streets he's won the Grand Prix here three times.

Hey, listen, do me a solid and check out a '89 Chevy Monte Carlo registered to a one Jesse Pinkman.

The Dutch Grand Prix, here at Zandvoort tomorrow, will be his first race since his accident at Monte Carlo in May.

We need mathematicians to come in and simulate this, to do Monte Carlo things to understand how these tools combine and work together.

You have to remember that at Monte Carlo, because of the nature of the circuit you shift gears over 2600 times during the race.

Reinforcement learning deals with an unknown environment, and the heuristic function and A search and Monte Carlo techniques are used to deal with computational limitations.

In 1975, I met in Florence a professor, Carlo Pedretti, my former professor of art history, and today a world renowned scholar of Leonardo da Vinci.

And there's the winner of the last three Grand Prixs, Scott Stoddard who's still recovering from his Monte Carlo injuries and leads the World Championship with two races to go.

Carlo, who used to be a Stanford professor for many years, then scanned his kitchen and with the same Harris corner detector was able to reconstruct a 3D structure of his kitchen, as shown over here.

Then finally another way to do abstraction is rather than considering every possible deal of all the cards, I can just consider a small subset of the deals to do Monte Carlo sampling over the possible deals, rather than considering them all.