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00:00:00
hello i'm about to start it caught from alan turing was we want
00:00:06
or want to that someone is a machine that can learn from experience
00:00:11
so now they are here and they choose this stunning performances and in
00:00:15
fact in only last five years their performance is very improve by ten percent
00:00:20
what what is the reason behind the recent fast progress of machine
00:00:24
learning outwards so let's summarise the training site cycle of machine learning algorithm
00:00:29
first together some data then we choose the model that fitting uh it fits better the data we train it
00:00:35
on that we train it on the data and then me test it okay so now we have lots of data
00:00:42
and indeed the size of the data that we have
00:00:45
is growing began exponential rate this large data enables us to
00:00:50
train more complex models for example teach learning to ignore networks
00:00:55
but on the other hand this is not for free and we need to spend more computational resources
00:01:01
now let's construe to example of classifying docks so at each iteration v. choose
00:01:07
one data point one dog can be off data model based on that the
00:01:11
next iteration be choose another data point and the optic them all but this
00:01:15
is not the best idea wise that uh because we might have similar data points
00:01:20
or after some training normal model already learned some part of the data
00:01:25
but it's quite sure about some other part of the data in my research we give develop i'll go it's
00:01:31
actually fast celebrates that on the fly chan i identified
00:01:35
these data points that the model is i'm sure about them
00:01:39
and use only them top data model so all the show
00:01:43
you to show both critically and experimentally that our mess out
00:01:46
there is the model be much less a comp competition resource thank you

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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.