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hello i'm about to start it caught from alan turing was we want
or want to that someone is a machine that can learn from experience
so now they are here and they choose this stunning performances and in
fact in only last five years their performance is very improve by ten percent
what what is the reason behind the recent fast progress of machine
learning outwards so let's summarise the training site cycle of machine learning algorithm
first together some data then we choose the model that fitting uh it fits better the data we train it
on that we train it on the data and then me test it okay so now we have lots of data
and indeed the size of the data that we have
is growing began exponential rate this large data enables us to
train more complex models for example teach learning to ignore networks
but on the other hand this is not for free and we need to spend more computational resources
now let's construe to example of classifying docks so at each iteration v. choose
one data point one dog can be off data model based on that the
next iteration be choose another data point and the optic them all but this
is not the best idea wise that uh because we might have similar data points
or after some training normal model already learned some part of the data
but it's quite sure about some other part of the data in my research we give develop i'll go it's
actually fast celebrates that on the fly chan i identified
these data points that the model is i'm sure about them
and use only them top data model so all the show
you to show both critically and experimentally that our mess out
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.
James Larus, Dean of IC School, EPFL
6 June 2019 · 9:58 a.m.
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
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.