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Romain Edelmann, EPFL (LARA)

Thursday, 7 June 2018 · 12:22 p.m. · 02m 50s

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i one mechanism and today i'm going to talk about are using a neural network to guide expression transformation

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so imagine you are given some kind of objects such as religious cubes on which can apply transformations

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and you are interested in finding pass so sequence of

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transformations which can go from one configuration to another

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if you blindly do that during to a problem is that the problem is that there

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are so many states and it's impossible to get him visit them all blindly

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so what i do in my work is that i use a neural network to guide the search and the idea is very simple so

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you take two objects you feed them to the network and used output

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an estimation of the number of transformation that must be applied

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in order to reach the targets and uses information we can devise not go isn't that we'll really approaches solution

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now in my work i'm not using rubik's cubes at my objects but mathematical expressions

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and they look something like this so your valuables addition

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and multiplication in addition it's expressions of focus

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which is where we can apply transformations the transformations we consider our

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commute activity as a city city and you should be t. v. t. v. and

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also operations which moves focus on the expression to modify different part of expression

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oh this is an overview of the system and it's very simple what we do is we take do expressions with a focus

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we feed them to us realistically um and requesting on network and whipped in points in a

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very high dimensional space and then we simply measures it the distance between those two points

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so we twenties network fun fun fun six million barrels of examples of expressions

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uh with distances ranging from one to ten and at the end

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we obtain it system that is able to approximate the

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the distance between expressions within minutes rudolph less than one

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so here we see the performance of a whole system that compared to breadth first search

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and we can see that our system is able to find parts between expressions um

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in both cases in less than one second compared to um the efforts in two minutes

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so this project is available online uh and get up at this address and was

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built using tightened by george so thank you very much for your attention

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yeah

Andreas Mortensen, Vice President for Research, EPFL

7 June 2018 · 9:49 a.m.

Jim Larus, Dean of IC School, EPFL

7 June 2018 · 10 a.m.

K. Rustan M. Leino, Amazon

7 June 2018 · 10:16 a.m.

Katerina Argyraki, EPFL

7 June 2018 · 11:25 a.m.

George Candea, EPFL

7 June 2018 · 12:08 p.m.

Utku Sirin, (DIAS)

7 June 2018 · 12:11 p.m.

Arzu Guneysu Ozgur, EPFL (CHILI)

7 June 2018 · 12:15 p.m.

Lucas Maystre, Victor Kristof, EPFL (LCA)

7 June 2018 · 12:19 p.m.

Romain Edelmann, EPFL (LARA)

7 June 2018 · 12:22 p.m.

Stella Giannakopoulo, EPFL (DIAS)

7 June 2018 · 12:25 p.m.

Eleni Tzirita Zacharatou, EPFL (DIAS)

7 June 2018 · 12:27 p.m.

Matthias Olma, EPFL (DIAS)

7 June 2018 · 12:31 p.m.

Mirjana Pavlovic, EPFL (DIAS)

7 June 2018 · 12:34 p.m.

Eleni Tzirita Zacharatou, EPFL (DIAS)

7 June 2018 · 12:37 p.m.

Maaz Mohiuddlin, LCA2, IC-EPFL

7 June 2018 · 12:40 p.m.

El Mahdi El Mhamdi, EPFL (LPD)

7 June 2018 · 12:43 p.m.

Utku Sirin, (DIAS)

7 June 2018 · 2:20 p.m.

Arzu Guneysu Ozgur, EPFL (CHILI)

7 June 2018 · 2:21 p.m.

Romain Edelmann, EPFL (LARA)

7 June 2018 · 2:21 p.m.

Stella Giannakopoulo, EPFL (DIAS)

7 June 2018 · 2:21 p.m.

Eleni Tzirita Zacharatou, EPFL (DIAS)

7 June 2018 · 2:22 p.m.

Matthias Olma, EPFL (DIAS)

7 June 2018 · 2:22 p.m.

Mirjana Pavlovic, EPFL (DIAS)

7 June 2018 · 2:23 p.m.

Eleni Tzirita Zacharatou, EPFL (DIAS)

7 June 2018 · 2:24 p.m.

Maaz Mohiuddlin, LCA2, IC-EPFL

7 June 2018 · 2:24 p.m.

El Mahdi El Mhamdi, EPFL (LPD)

7 June 2018 · 2:24 p.m.

7 June 2018 · 2:25 p.m.

7 June 2018 · 2:25 p.m.

Erik Meijer, Facebook

7 June 2018 · 2:29 p.m.