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00:00:01
hello my name is then you are only pages you in in in the lab um i do my research
00:00:06
machine learning and one of the problems i'm working on is how to search in a database when it's hard to
00:00:12
describe what you're looking for in details indeed most of us here would struggle to draw
00:00:17
a picture of a friend accurately enough that it can be used as a aquariums for search
00:00:22
however if i show you two pictures of your friend of someone else you would be delayed
00:00:26
some increase to this is to not only for pictures but for many other things like videos music
00:00:33
of um fluid and so on so people are good at making comparisons now suppose
00:00:38
that there is a database of some comparable objects anyone's find a particular object there
00:00:43
but without explicitly describe what it is just to be more concrete let's say
00:00:47
there is um database of movie actors and you want to find this particular
00:00:52
i'm late on the slide if you don't remember her name but you remember how she looks like
00:00:57
um than what we propose in our search algorithm is to review
00:01:02
example from this database and ask you to choose around the object from this sample which is the most
00:01:09
similar to your target so which one of these four people looks more like that lady on the previous slide
00:01:16
and then you give us your answer would even other sample and so on until we find your target uh so that's we
00:01:23
um our algorithm allows you to navigate through the database by only comparing objects from
00:01:29
this database to target but without explicitly describe it you target um our algorithm also
00:01:37
uh at lawrence its own feature presentations but again only
00:01:40
from the comparison data and without any feature extraction methods
00:01:44
and in fact if you look at the two d. um features
00:01:49
a stadium batting of four of the features that this out with learns
00:01:53
uh you'll see that people who looks alike they're actually being and um clustered together
00:01:59
so we build up this website was that um these uh um go there and visited a um
00:02:05
if you want to look up forks movie actors and or just check out our search algorithm you're welcome thank

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Conference Program

Welcome address
Martin Vetterli, President of EPFL
6 June 2019 · 9:48 a.m.
Introduction
James Larus, Dean of IC School, EPFL
6 June 2019 · 9:58 a.m.
Introduction
Jean-Pierre Hubaux, IC Research Day co-chair
6 June 2019 · 10:07 a.m.
Adventures in electronic voting research
Dan Wallach, Professor at Rice University, Houston, USA
6 June 2019 · 10:14 a.m.
When foes are friends: adversarial examples as protective technologies
Carmela Troncoso, Assistant Professor at EPFL
6 June 2019 · 11:09 a.m.
Low-Latency Metadata Protection for Organizational Networks
Ludovic Barman, LCA1|DeDiS, EPFL
6 June 2019 · noon
Interactive comparison-based search, and who-is-th.at
Daniyar Chumbalov, INDY 1, EPFL
6 June 2019 · 12:06 p.m.
Decentralized, Secure and Verifiable Data Sharing
David Froelicher, LCA1|DeDiS, EPFL
6 June 2019 · 12:09 p.m.
Communication Efficient Decentralised Machine Learning
Anastasia Koloskova, MLO, EPFL
6 June 2019 · 12:11 p.m.
Detecting the Unexpected via Image Resynthesis
Krzysztof Lis, CVLab, EPFL
6 June 2019 · 12:14 p.m.
Sublinear Algorithms for Graph Processing
Aida Mousavifar, THL4, EPFL
6 June 2019 · 12:16 p.m.
Protecting the Metadata of Your Secret Messages
Kirill Nikitin, DEDIS, EPFL
6 June 2019 · 12:18 p.m.
Teaching a machine learning algorithm faster
Farnood Salehi, INDY 2, EPFL
6 June 2019 · 12:21 p.m.
Secure Microarchitectural Design
Atri Bhattacharyya, PARSA/HexHive, EPFL
6 June 2019 · 12:23 p.m.
Security testing hard to reach code
Mathias Payer, Assistant Professor at EPFL
6 June 2019 · 1:50 p.m.
Best Research Presentation Award Ceremony
Bryan Ford, Jean-Pierre Hubaux, Deirdre Rochat, EPFL
6 June 2019 · 3:54 p.m.