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the morning everyone and uh thank you for the invitation i'll for attending today
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well i that's not to say it
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uh
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this is a domain that that is not new and i'm going to show you that and um
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it's becoming again of interest and uh as a for many older issues in
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a machine learning which were dormant for some years in the winter okay okay i'm
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and uh and now we are and it was winter era principle or hopefully not in uh
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you prepare winter okay so i'm just
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uh some few words about my uh
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the long dimple these uh you see a lot of levels inside well actually uh that
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we belong to the h. e. s. is all either don't we have the i.
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c. t. departments and an i. c. t. is it that where we have six
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research lines or interest groups and and
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of the usually the biomedical applications domain but and very close to the
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italian data analysis um access also so uh why that the because my group
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i call it computational intelligence for computational biology as it represents
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very well that name what we do what we do is
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it's coming from the competition italians domain we apply to the computational biology period
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for your competition intelligence in principle is you really know what to say it computational
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biology is the study of biological systems or leaving systems in general by means sauce
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computational models that's a very shortly and it's a includes
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the by informatics part and that explains also why my group
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belongs to the eh uses it to for the informatics but
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i'm not going to speak about that might interest today is
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to give deal
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my view on the old area is a limited you but it's what
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we uh uh more or less try to do we made in our group
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and uh and perhaps going to start with a high a male and modelling um
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very shortly in the brain introduction it was mentioned that i'm uh electronic engineer
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i'm also a a dietitian so i have
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a strong not strong but uh a v. mainly
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a a background engineering and um because of that
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some of the ways i see the domain or right
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a deal with the domain are related with my past uh or we are all slave for last
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so i'm going to speak a little bit about my d. h. m.
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m. how a i. m. l. i. modelling a related then some words about
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understanding explain interpreting what well what we're looking for and then
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hopefully this the the centre of my presentation would
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be these two aspects interpret the role models and
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model induction and at the end i charge view of what we we we we are trying to
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deals from at this moment sell to start with this year's part a i. m. l. m. modelling
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i just
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given of more because of my uh ultimate edition
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last i have the tendency to see everything as a system and
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every in output relationship as a model so but i'm not the only one
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if we take these uh definition which is very common about a machine learning machine and
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explore the study of consumption of algorithms that can there from and make petitions and data
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so okay that's the classic we have data and at the end we had
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prediction on this issue also some kind of behaviour that's uh but the same
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definition continue saying that size others operate by building it more little from example input in order to
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make data they're even traditions or decisions so we can say that what is inside it's a model
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it's some kind of representation of a reality
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that can be a very straightforward uh uh
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we have a decision making process and we have people to have all
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puts but can be more complex we can have a complex more complex systems
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which are the reading of the data and we have to build models of a one part of that
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so that's my vision we have they that we have some kind of output and in in the me that we have a model
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so yes machinery is model based and based on that i can go a
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little bit farther and say okay machine there me is similar equivalent or whatever
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to modelling so the same concepts we have been applying for modelling for many many years
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could be it's somehow applied to understand what maturing is and
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from that point of view when we
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when we uh the see that doesn't modelling the process east okay we have s. m. although
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the model has some kind of input
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and it produces some kind of outlook on the output can be as
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i mentioned something abstract like a behaviour
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or or a production or whatever and
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let me can with uh some approaches or some view of
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what uh how do we do a moderately and that's a classic
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wait where you have somebody who knows how to build models the discuss discusses with
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the expert in the area and then it's pro he produces m. although he or she produces a model and
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in a sort of a classic design lot we have this model but
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always we need to validate the model in this k. with the expert
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that's uh what they call human breathe analysts based
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hi for that to start the timer or a more conventional or more data driven
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database modelling we have data we have to integrate some day this data in some way
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um some kind of a processing and at the end we hats
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and modelling tool which begins and more they'll based on
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an algorithm and iteratively very often iteratively so was too
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reviews the difference between the data the target and they'll paint or the the
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the predicted behaviour that's a high view of a more than process and
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always given that we are modelling just just for the sake of modelling
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but for some kind of goal we have to validate the model corresponds
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to uh what uh the expert could the text expect from uh from that
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so
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we can see that in a more general way with haas data different
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kinds of data sources we have a conditioning issue we have this part which
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this same problem is the most common parts that are built in a
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modern approach and for me this important to see that we have really too
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parts even if we don't often think about
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modelling us that's these two separate parts we have
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and more they'll that kind of model which is a representation of the of what we want to
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to uh to model and an algorithm responsible for building the model and
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uh from my point of view
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having these two parts
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define i given machine approach so very often we speak
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about the model uh actually knowing approach as a monolithic methods
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but if you look at it you can say you can see that there are really two parts
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the representation and the building agree and very often
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you are working with the single representation and we are you are changing
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the strategy of that great big and that's my point of view that
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any much in learning approach can be seen as these uh these two parts and uh i
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i have been uh is some sometimes see with my students doing the the exercise was okay take
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the top and matching larry allowance or whatever at least of machine
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learning algorithms you want to and for each one try to identify
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these two parts actually we try to use to to
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add a third elements the performance but that's a handler
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and just tried it will the that exercise and you will see that in principle all is uh
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separating these two parts even you sometimes they are very closely uh and
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related okay so now let me go to understanding explaining and interpreting
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i could call that the understanding understanding understanding
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explanation were explaining explanation or whatever kind of uh
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ward a freak but mainly is what are we looking for
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starting with the this point where we have another into built and i model
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i can say that they interpret the ability of the decision
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lies mainly or depends mainly on their presentation then
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their presentation is a very important for having some kind of explanation and from their
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presentation put point of view there is a classic distinction of a three kinds of uh
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presentations we have the white boxes where you have
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a complete due off every detail on the relationships on
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the elements on the system or the variables in the process or whatever kind of um things are you modelling
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classical you can have for example a on or you know essentially question and you have to you
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don't divide parameters uh idea put any parameter here that's not a very good example but usually about
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constance multiplying and adding and that's a classic a white box and the
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other side we have asked how you know very well neural networks which are
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the prototype of a black box it's but it's not the only kind of black box without and
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as soon as you have what white on black why not having the continues of great and that is
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a lot of uh different approaches where we are in
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the middle of a these eh to uh i extremes
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these three approaches can also be a related with these
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tradeoff between production and explain ability we can also that
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all that performance and interpret ability and different names and
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see this is real for life sciences where's or a
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work that make any steam all those are very precise
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but they are not very productive very precise because they describe exactly
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the process but they have a very productive because they are extremely local
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so all they predict something very local but they are not really able
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to capture relationships a a larger scale and usually they everybody predicted but
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for the expert in the domain the question is very clear as describing
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very very clearly so they are very explainable but it's usually whatever the cliff
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on the other side you have black boxes which it
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if they are well don't has a you know how to
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and then you have a very good production with a generalisation or rotation whatever kind of uh
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protection against the overwhelming but it's effectively with and have some kind of text
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inability and in the meanwhile we have these middle usually you will find it
00:14:50
kinds of mall there that are more or less interpret double or that are more
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of it it's a predictive and you have this kind of that continues eh eh
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transition from one extreme to the other
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mm that's
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putting explained ability the next but what's
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what's that x. how to make sure that that's one of the
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open questions and even if we use think on what do people expect
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and i'm well placed to describe that goes in my projects i have to
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my parameters are biologists uh an regular
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just all kind of the life scientists
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from uh and clinicians to a scientist and
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when we speak about some kind of some level of explanation we can
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dolls from very simple things like which are the more productive but uh variables
00:16:04
i mean i'm going to speak uh to present a ready rapidly tom somebody
00:16:09
somebody market discovery and the idea is to find the two three twenty fifty
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by your markers that that the more descriptive for predictive
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uh for the given deceased for example and that's already some kind of explanation and
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a lot of work is down from the past that very often we have to
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transform our our data so was to extract information that's what
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we call a feature extraction teacher during and for many people
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just what are the most informative features that can be used for
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making a good production so all i have to apply that kind of filter that kind of a a transformation
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uh or that kind of a preprocessing and that's the second level
00:17:05
the very also in um personally i'm very interested in the functional relationships between
00:17:12
variables feature or font all that so that we have
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i call functional relationship for that can be a a logic
00:17:21
that's involves or equation simple equations or
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linear combinations and that some kind of explanation
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uh i in leaving that very abstract because functional relationships can be
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as white boxer say the differential equations
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or ass black box as simple correlations
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so it's very hard to say what is this
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kind of but relationships are much more informative that simply
00:17:56
a static list of variables or features and
00:18:01
many people are interested then in the mind of many
00:18:06
people when we speak about explanation very open we expect that
00:18:13
re some explanations so i'll
00:18:17
they are more though why the view predicted that uh that asian
00:18:24
was a sick of uh that this this and we
00:18:30
were we we should expect something light the our user
00:18:36
i decided that because he presented that and that and that but that's
00:18:43
a whole domain and the uh perhaps that's something that we have to
00:18:49
be able to from now to the future we're not close to that
00:18:56
even if i'm saying going to mention a little bit later
00:19:01
the beginning of or to be sending billions were was was more based on that
00:19:08
part than on the older i mean there was a lot of
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human like logic
00:19:18
uh in the systems that were able to produce some kind of re some
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explanations but they were very hard to be helpful to magically as we are useful
00:19:28
okay
00:19:30
we have that model but forgot more that we can also have
00:19:34
this got other kind of a view what the went to explain
00:19:38
we can and that was my view uh in principle uh when
00:19:44
i started is the main that we are having no one explanation
00:19:50
i mean i have a flag must uh lung cancer agnostics system and i would
00:19:55
like to see rule this describing these are these are the rules describing one cancer
00:20:04
and that's nice but
00:20:08
yes let's go to a mixed for your domain and ask him asker
00:20:13
to give you have full list of criteria going to the egg knows when cancer
00:20:20
usually is not very common to have all picture that very often or or forced
00:20:27
very specific things quite often we are interested in global explanation we're explaining the whole model
00:20:36
uh i'm trying to explain the whole model vendors to understand the form of on the other side
00:20:45
much more often assumed example i try to use rate uh uh
00:20:50
something say go off we are more interested in explained as he
00:20:54
just why did you say that what the predicted that why did
00:20:57
you propose that at that moment we are explaining that specific decision
00:21:04
and that these are local explanation this patient has cancer because
00:21:11
in the rodeo in the eh in they mess i can see uh that kind of remote do uh which is a very
00:21:18
big or which is very regular and whatever kind of uh and
00:21:21
that's another kind of explanation and that's i think that's more realistic
00:21:29
but as usual if you oh whoa
00:21:33
if you will you will very specific tool every
00:21:37
case then you lost some of the global view and
00:21:44
you see that it you you can see that is a continuum
00:21:47
and perhaps for for some cases the optimum was found in between okay
00:21:55
and i think i have to accelerate a little bit
00:21:59
some approaches i mean uh taking this a images from the
00:22:03
uh uh material from the darker program unexplainable activists and variance
00:22:08
they proposed initiative to develop this kind of systems and
00:22:14
they say okay for then there are mainly two families that this what for me it's uh the more clear what
00:22:22
you the fact he didn't divide families these are there are models which are interpret that will from the beginning
00:22:31
yeah
00:22:34
that's what we call the ad hoc approach the models are built to be profitable they are imagined to be interpreted
00:22:43
and there are also approaches the other side that are
00:22:49
five history is do they have that we don't know the induction that's what we call the post talk approach where
00:22:57
we are interested in extracting some explanation elements for blood from black while that's
00:23:02
we are how we accept having backboard models but i given moment we bailed
00:23:07
an explanation part from this model and all that nowadays
00:23:13
uh they are based on the blaring because it's uh
00:23:18
the new kid on the block and uh that's when dave point of view in our group
00:23:25
we work with wall to wall warts uh in the interpreter
00:23:31
model side um i've been working with the box models from uh
00:23:37
the uh twenty years now and these are models are conceived to
00:23:41
be interpret double and all the other side that's where i mean
00:23:47
presenting today as the the centre part some
00:23:51
approaches for mulder induction where from the people earning
00:23:56
uh we are extracting explanation elements from like models we are doing
00:23:59
that and that's what i'm going to present so let me start
00:24:04
with interpret double models or the ad hoc approach
00:24:09
as i mentioned this is not a new topic
00:24:12
if they go phi would all fashion artificial intelligence
00:24:19
was already strong oriented towards you my representations expert systems uh
00:24:24
uh or kind of a search algorithm some rule base three is graphs et cetera
00:24:29
what are what we're oriented to capture human knowledge bad approach was uh
00:24:37
human driven and very every we ah
00:24:43
we could we could find some data remain approaches used to improve for you to
00:24:48
adopt does uh those uh more those which were built on that so that's not new
00:24:55
or the black agree white box issue that i present some minutes ago
00:25:01
was already bore the last century activists in the networks in
00:25:05
the sixties fuzzy logic in the sixty decision trees in the seventies
00:25:09
what formally uh oriented hours learning because decision trees have been
00:25:14
there uh for longer and the white box over the
00:25:18
base models about that they have been there for much more
00:25:23
a lot of years in that last real winter era nineties painter was already there
00:25:31
there was a lot of warts on accuracy was interpreted
00:25:34
willing to trade off road induction wrote distraction out around
00:25:37
the time i was doing my p. h. d. uh there was a it was if more or less a subject
00:25:44
uh the of interest to us a lot of people doing that and as mike you mentioned
00:25:51
these domain the camera
00:25:55
uh and standby for several years and now we are again revisiting that
00:26:02
uh under the new i might also mention
00:26:06
my only research was interpret ability oriented fuzzy modelling
00:26:12
and for my research i compiled so my interpreter will speak period
00:26:17
it was interesting for me i well i went to a tutorial last year on interpret double falls models
00:26:25
and eighty percent of the criteria were presented by the where the same or very close
00:26:33
to what i come five at that time is not that i invented forever just the the
00:26:39
that's these are constants that are still valid and on that base i propose an approach could
00:26:48
these oriented that we interpret abilities and the hawk approach i mean the more
00:26:51
those are built to be interpret double and the model yep a process is
00:26:58
constraint to respect these a criteria of interpret ability so
00:27:05
this is a an approach um
00:27:11
in the current two thousand and it was called for the cocoa um diversion idea
00:27:18
propose other thing was on my that i um when i start to the the
00:27:25
uh the verbal we uh built a new version basin c. plus plus
00:27:31
c. plus plus which we call fuse for a fuzzy you need a genie
00:27:36
eugene unity can gene and uh then for the project we probably built also um there
00:27:43
that code and now we are going to or spite on trying to keep
00:27:50
uh connected to what is a used wisely and
00:27:56
um this approach has been used to uh
00:28:03
as i mentioned for for example for violin diagnosing by market discovery
00:28:08
as heavily as a two thousand and seven we have a product for
00:28:13
selecting by market for about a catchers cancer screening in two thousand nine to eleven wouldn't probably with
00:28:20
uh interested in profiling by the markers for every don't cancer
00:28:25
uh in this e. two years we wear involve also uh
00:28:31
in colour rectal cancer screening unlucky mess of typing this was
00:28:37
our pharmacy aware and team of uh both my oldest and a commercial partner in that
00:28:46
pardon was only a commercial partner interested in
00:28:51
understanding the role of lung cancer in that project
00:28:56
uh this was a company producing or and now they are commerce advising a screening test
00:29:03
and here it was separately with the hospital interested in having a new test of a forgiveness
00:29:10
of dating scene the there so just to mention that even if you that is an approach
00:29:19
conceived
00:29:21
and bore
00:29:23
twenty almost twenty years ago it has been able to deal with the real world problems
00:29:30
perhaps all of them are small data problems but in life
00:29:35
sciences we are very often confronted to that i think the third
00:29:40
of your uh war shops war was on the small data exactly because for many areas
00:29:47
big data is not realistic and we still have
00:29:51
to produce productive systems for that so i that was
00:29:58
um view of what can be known
00:30:02
i conceding directly more there's the which are uh um which are interpret that
00:30:09
will perhaps one mile to do that we were obliged to deal with it
00:30:16
gene selection thing or feature selection you know which is one of the first steps i mentioned for understanding
00:30:24
is which are the most relevant variables and if you are speaking about
00:30:27
by marker selection you're clearly interested on uh having this uh variables so
00:30:35
it made me go on then to the more the induction part the post hoc approach based on
00:30:42
the plumbing and there i'm going to the to
00:30:48
all through a two percent patients the first one
00:30:55
is uh using or exploiting the local and global
00:30:59
internal representation about something that is very common nowadays and
00:31:07
well we start that's always from the cons the the
00:31:12
the fact that there are the words are black boxes and we could be interested in obtaining some understanding
00:31:19
and it's not clear examples are uh going to filter activation
00:31:23
or having some kind of a sideline see uh uh um detection
00:31:30
and
00:31:33
based on that we can already be in some kind of a basic rules
00:31:39
having a the and some kind of filters than for this class those for him to reply as we can
00:31:44
see that uh there are some features that are more
00:31:48
activity than all others and we have a class activation map
00:31:52
where we can see which are what are the importance of different filters and
00:31:56
these are based on the take the local uh that's a relatively recent approach
00:32:04
and now we're group we we're also we well we also explore that um for them all we'd
00:32:12
be of an emotion detection that uh that exist for of for a long time perhaps a comment
00:32:19
uh from the for this research we are saying okay there is a lot of people doing
00:32:26
uh deploring yes let's let's use what they did we our remote exploring
00:32:33
completely new the blurring of architectures we're using
00:32:38
common architectures and common solutions like that one but
00:32:44
what we did just for them all was to event divider regions aware
00:32:49
more use by the classifier to detect the motion to say these is uh
00:32:56
but uh i was it's not sad about
00:32:59
mainly angry face this one also and say that
00:33:05
like the information use mainly relate mainly plays around
00:33:09
the mouth on the on the ice and bad well that's uh
00:33:14
we can also that for localisation of objects into an an image
00:33:18
like a a tiger here even the tiger is what i am and
00:33:24
is this a a wreck you know graffiti some interesting
00:33:28
uh events or uh uh elements in the in the image
00:33:35
that's something that we really the but we worked in our view is
00:33:41
of having an explanation if for example if i have to identify a lion
00:33:46
i can say this is a lie on because
00:33:49
there are some a few are we identify this pause the mouth
00:33:54
here which are typical of a lion's i've given that we have
00:33:59
uh for all four of them or several of them we can say this is a lion
00:34:05
i then is not only localisation but we need some kind of know
00:34:10
what explanation to say okay all of them of the unnecessary these are simple
00:34:16
and the f.
00:34:19
they wait we can x. the scene in a network that
00:34:23
it which are the elements is something like okay we want to
00:34:29
maximise the class of a dog even if he's not the
00:34:32
dog and we can say we can see that some of the
00:34:36
points of the elements of the image belongs to the dock
00:34:40
it took a a class but they're all there is to
00:34:44
these red one which are not belonging to the class and because of that perhaps it is not classify that's about that's
00:34:51
the uh respect activation mixing the station and a bit classy
00:34:57
way if i'm a minimising the uh some kind of loss
00:35:01
function and the problem is that the images that are he
00:35:05
didn't divide or that maximise the class are not very clear
00:35:11
and there are some adaptation of chain going from minimising last to my c. magazine to maximising the selected
00:35:18
out with some of the kind of optimisation function objective
00:35:22
function and then we can have some images that are
00:35:26
sometimes the or sometimes a more confuse of using about that uh mm
00:35:37
people trying to do a image reconstruction we're not a remote very satisfied with this kind of results then
00:35:45
the transitional was made from classical regular racers to using
00:35:51
neural networks themselves us regular ice so we are having the rio
00:35:56
their neural network to predict and then yeah at the neural network too
00:36:00
uh avoid the or to regular ice the they're the and
00:36:05
that's the famous very well noun gowns and that's what we
00:36:14
we implement it so we have a classifier that was
00:36:19
trained with uh the classic way and then we implement
00:36:25
another network which tries to maximise and even platt's and we are trying to
00:36:34
use that know what's a generator to find image that is
00:36:38
able to maximise this class and minimise the other for it
00:36:43
and that should be any image representing exactly that emit that the
00:36:48
class and with that we can obtain resource like uh for your art
00:36:56
you see the email just a remote is you will have us to see you hurt
00:37:00
but you will have elements that are found several times that are
00:37:05
classic for work and all we clearly see that are some images
00:37:10
that are clear of a work i know some of them are serial to the gays are but the gaze
00:37:16
or meets all other forms and for each class well
00:37:20
leave lemons are very clearly represented here and these are
00:37:25
images that are generated artificially to maximise the clatter
00:37:30
was learned by the the network so we are producing
00:37:37
the
00:37:39
in much that could be the ripper said the group
00:37:43
presented a common parts of all the images that were supposed
00:37:47
or worse were the say to have this class inside
00:37:52
it from that point of view that's clear for us i
00:37:58
that clear use we have we can see elements that are very
00:38:02
clear represented representative but sometimes we also discover some biases for example
00:38:11
for the second let me go on there are always elements like ice
00:38:17
more than the class they only they the the element
00:38:20
itself why because almost any image presenting that will also contain
00:38:27
ice or for the mean is scared we are very open a painting
00:38:32
uh uh uh also part of the core of of the body which
00:38:36
are uh not only representing the the object for the the same for uh
00:38:43
for a musical instruments very often the they are not completely separate and that's also a good
00:38:51
way to say okay the images because of that we can save this classes no represented by bits
00:38:57
only the all the but but its functionality perhaps the classes not will affect mm
00:39:07
but this approach
00:39:10
can be also used
00:39:12
not only for the class we could be also interested what are the images maximising this
00:39:19
filter for example if like identify the filter as being very active for a given class
00:39:24
then i can go and see that he may use active
00:39:28
making a lot this filter are composed of lines ah okay
00:39:33
we can see that these filters i was busy allies in vertical
00:39:37
hurries on the up and some kind of callers i we can
00:39:43
at the moment
00:39:45
uh concentrate on what this filter contains and what is called is a capture by
00:39:50
the network we already trained but we can also go to filter stuff which are low
00:39:58
low level not exactly the output but and see that some features are spacey allies for example
00:40:05
in uh these forms or in uh i use
00:40:08
or even a diagnosis and whatever that beard heads
00:40:15
and
00:40:17
that are the elements that could allow us
00:40:26
if i have a lion
00:40:29
is it composed of these four elements and these elements could be contained in this
00:40:38
intermediates layer and that's effectively what we propose and we would we the the
00:40:44
a more recently for for example for this class the
00:40:49
beagle we detected that the most active filters is uh
00:40:56
are activated by long years and this is a locally presented here
00:41:02
for that image and another filter very active was that one hand does filter
00:41:08
the text colour partners of the for and this filter the
00:41:13
texts that notes and in that way we can say if
00:41:18
in an image i have long ears specific colour part there and diagnosis then i half below
00:41:27
some other examples and we're going to stop here but for soccer ball we have quite serious
00:41:34
octagonal patterns and some grass and you see that grass it's
00:41:39
important for detecting a soccer ball from the images perhaps that
00:41:44
he well the information that the network image band we don't have soccer balls which are not
00:41:50
in real situations uh perhaps that's a bi yeah that could be corrected to improve our database
00:41:58
or for this a bear also we have
00:42:03
their heads with fee there's an some black and white colouring and that
00:42:08
combination is able to i we're selecting here the three most active filters
00:42:13
for that image and you see that we are combining loco
00:42:19
information with some kind of be mobile information about this local
00:42:25
and the the way we are combining both of them and that give us a good approach to fix that the spanish
00:42:32
here are some references but well that's not so
00:42:37
okay let me see if i can go back to my
00:42:41
or the presentation
00:42:48
okay i have to
00:42:51
finished up one
00:42:54
i think that we can go come to the okay so now let me speak about rule extraction and for that
00:43:04
'cause i have to finish it varies from
00:43:10
a exactly like i think it's still have um some more minutes
00:43:15
but well uh i try not to repeat where the same things but
00:43:21
the idea is okay from that point we can get some understanding combining
00:43:27
local i'm going to represent patients that's uh what is is already uh
00:43:32
of uh i have to make that and that's the approach a mention
00:43:37
i'm not going to mention it again but we're also through real another approach
00:43:43
the other approach is okay we have for example this kind of a network is the the you sixteen
00:43:50
and but i was layer that are performing feature extraction but
00:43:55
at the end there are for the connected players three layers
00:43:59
which are making the decisions the final decisions are made at that level
00:44:05
and
00:44:06
these little is composed of three consecutive normally i read this and that is that is
00:44:12
a there's a lot of a new rooms inside and that makes make it hard to
00:44:19
understand what kind of uh
00:44:22
what kind of a relationship is second thing inside so the
00:44:27
proposed approach is okay is is the idea is very simple
00:44:31
let's say okay instead of that
00:44:36
or or we are trying to find
00:44:41
an equivalent to interpret obliquely wanting to these three layers is
00:44:45
not that i'm replacing a replacing then for a explanation issues but
00:44:50
if i want production i will use my my network lineup but for explanation i can
00:44:58
replace it would use that to generate some kind of
00:45:02
a rule based to a representation and then again we are
00:45:09
going into the representation pass the central issue for it but that really are in that case
00:45:19
then a little restraint and we have some
00:45:25
feature active i activation some filters and we replace
00:45:29
the last convolutional layer by um
00:45:37
around the forest
00:45:39
random forces not interpret double per se because we have a lot of uh different
00:45:44
uh trees and it's very hard to find a global explanation but we can on the base
00:45:52
into the into the into our database we can see which are the
00:45:58
if the rules contained in these forums that are the most active and we can create the ranking
00:46:06
they went to lose a ranking is not a going and looking up
00:46:10
these activation is we have a simple perception which is combining all of them
00:46:17
trying to obtain them be maximum performance with respect to the
00:46:21
network i mean the the maximum of predictive power had the weights
00:46:27
of a connection for this perception represents
00:46:31
are used to rank the rules very simple and the way you can have it top rule the second of on
00:46:38
and the idea is that somebody does the human part is taking
00:46:44
the top for a top five to seven rolls and then with that
00:46:50
uh and my in mind we can use for example here all
00:46:54
four is not a very good example five of the five most uh
00:46:58
important roles we're threes are saying about classes is that class yes
00:47:03
yes we can say this is the label and these rules are explaining
00:47:08
the um the decision with a very relatively good uh precision results
00:47:17
we can see here for different lot different classes if we use the top
00:47:22
top uh the top rule the top three rules top five uh three four five six in that
00:47:29
way and you can see that for many classes the top to the rules are already very good
00:47:35
and for some others is necessary to go to five
00:47:39
six or seven rules and these rules are are composed
00:47:46
um sometimes for for some classes we observer that three rules maybe
00:47:50
more upgrade the fifty rolls for example because uh some rules are
00:47:56
uh per topping the decision
00:48:01
and as i mentioned some in some cases to go five a remote not enough
00:48:09
the rules ha of uh this cat use for for example the top the rules for the l. class
00:48:14
are the the feature x. four five a four hundred and fifty is bigger than two and
00:48:22
that's that can be expressed as a level of importance and these
00:48:29
features can be be sliced with uh the other methods we use or
00:48:35
we can use the same um activation of each uh theatre
00:48:41
to create some kind of maps where well it's not very
00:48:46
we have these fears fears to roll a reason
00:48:51
which is the second four hundred and fifteen four hundred and twenty seven okay
00:48:56
and we see that oh these are where both classes are more active and this is the
00:49:04
the threshold and we obtain that special for this we can go
00:49:12
look at the all the rule and forgiving more detail
00:49:16
if if we can see that effectively these are clearly
00:49:22
the all we are interested in and more we are going
00:49:25
to close to the the threshold the less we are having clearly
00:49:31
images and these are all the images that were used for training
00:49:35
i mean if you have a single image you will find it
00:49:40
in a given point on for that image you can half the level of population of each of the
00:49:50
some uh
00:49:53
he's here we have things that are clearly not calls but because the the or close to the the limits
00:50:01
or that belongs to different kind of calls
00:50:06
that allows us to eat all the lisa model here
00:50:10
which is uh that part is a liar but because of the two circles it could be similar to two guys
00:50:19
another class a goldfish were the same we have the clear goldfish we have other kind of features which
00:50:26
are not of the same class at objects that are come clearly elias because of the court or whatever
00:50:33
and some classes are higher there
00:50:36
and uh we mention the some classes they asked for four five seven rules so
00:50:41
i'll single rules are not very clearly and we have a lot of all liars
00:50:51
but even there we can try to just see why these are here these are highly mostly water
00:50:58
here we have a total scheme and uh the
00:51:01
and here we have the clear purples because they are
00:51:06
they have total scheme and they are in the water so if you don't for that class
00:51:11
that's a method
00:51:13
uh uh some
00:51:16
comments about the the way it was used the two
00:51:21
regularly station uh to the last function that were used for regular decision for minimisation and
00:51:28
we have a ranting of rules and the preferences provided by the human
00:51:34
the human is a saying okay uh we're interesting three or five or we
00:51:39
i think three are no for that that's just remember that we were here
00:51:44
exploring a lot of different classes but if you are interested
00:51:48
in uh in grants perhaps you're interested in three or five
00:51:52
classes of words and not in the fit and not in every anymore in the in the the images are mm
00:52:02
for me it's clear that these levels are very close to what i have been
00:52:07
doing for a long time the fuzzy logic where things are gradually changing from uh
00:52:13
the from zero to one instead of a given of threats will so
00:52:18
perhaps with fertility we could we could have rules that better capture they'd
00:52:24
the eh
00:52:26
this mall um changes between the the matches we belong to the
00:52:32
class okay okay let's say that that was all for the second approach
00:52:41
and now
00:52:44
uh the last thing i would like to discuss very shortly with a single slide
00:52:51
case what do
00:52:55
where do we plan or wish to go from now
00:53:00
an example in my domain of uh life sciences is uh
00:53:05
the reading about the classification that was a present the very
00:53:09
uh well some some days ago only by uh you all with a partner
00:53:16
and um well we were already thinking of that but there's a lot of people working on the the very good example
00:53:23
is to classify it to classify a given an a rating
00:53:27
of graffiti uh us being everything about the or not and
00:53:33
our first step that our core research is protecting loco
00:53:39
a class relevant features and produces some kind of rules
00:53:44
of grammatical instead of uh trying to you in for them
00:53:50
and
00:53:52
in that way we have these two parts the most focused lottery methods and the explanation
00:53:59
based on roles this is our current research but we are aware that that's not enough
00:54:07
and at a given moment to say that as an extra
00:54:11
day and that our cotton world sports we need an experimentation
00:54:17
i don't think at least for the moment that we could
00:54:20
automatically go and look the villager tour and even defy what we
00:54:27
learn here as being extra days or couple so
00:54:31
we need some kind of experimentation and include that
00:54:37
in the modelling process so was too
00:54:40
try to use things that belong to specific classes and
00:54:45
nothing that we discover unless they are but so important that
00:54:48
we propose that to to the expert and in the middle of that we have a we need some kind of um
00:54:55
of a quantification of the of the explanation of the
00:54:59
quality of the spanish and and all that would be used
00:55:03
to produce final explanation that could be pro uh close to the uh
00:55:10
recent uh explanation that they mentioned the beginning something like if the number
00:55:15
of exceed eight is a bigger than one and the number of quota will sports is bigger than two then the patient houses with you know but
00:55:23
that kind of role is not something that we are close to obtain and
00:55:28
we know that we are only here and we need to continue uh uh
00:55:35
improving our methods here and also i'm adding new uh explanatory
00:55:43
ah issues so as to see going to rio explanation is still
00:55:53
a long time um uh for some things we are not so far from that
00:55:58
the system that we were presented last week or of the two weeks ago uh use
00:56:06
an intermediate uh learning part where
00:56:12
are they uh are partnering with the to be hospitals in the
00:56:16
us they had a lot of expert undertaken that and they are producing
00:56:22
an intermediate step where they say okay our image you know wearing that we have the amplified that kind of thing that kind of a
00:56:28
the kind of thing and then this part was not a simple
00:56:32
rule uh it build and as we proposed here but they need again
00:56:38
and they're mean on from the the feature that were
00:56:42
distracted we didn't divide to the decision so they are
00:56:47
doing the some kind of a deep learning for you to define the
00:56:52
the events and then another part for you define the
00:56:57
relationship between that so automatically going for a mention finding
00:57:03
interesting features finding the relationships between these features
00:57:08
and producing every sound very uh explanation that seems to be the state i haven't
00:57:17
had access to uh who the specific to uh have is there still a
00:57:24
okay only speaking about what they uh they can be they
00:57:28
can do but don't know exactly how about something to follow because
00:57:33
that vision within three uh having access to a lot of data as is the case in the us
00:57:41
yeah this facilitates a lot of things this kind of results i well i think

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Conference Program

Methods for Rule and Knowledge Extraction from Deep Neural Networks
Keynote speech: Prof. Pena Carlos Andrés, HEIG-VD
3 May 2019 · 9:10 a.m.
349 views
Q&A - Keynote speech: Prof. Pena Carlos Andrés
Keynote speech: Prof. Pena Carlos Andrés, HEIG-VD
3 May 2019 · 10:08 a.m.
Visualizing and understanding raw speech modeling with convolutional neural networks
Hannah Muckenhirn, Idiap Research Institute
3 May 2019 · 10:15 a.m.
Q&A - Hannah Muckenhirn
Hannah Muckenhirn, Idiap Research Institute
3 May 2019 · 10:28 a.m.
Concept Measures to Explain Deep Learning Predictions in Medical Imaging
Mara Graziani, HES-SO Valais-Wallis
3 May 2019 · 10:32 a.m.
What do neural network saliency maps encode?
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3 May 2019 · 10:53 a.m.
Transparency of rotation-equivariant CNNs via local geometric priors
Dr Vincent Andrearczyk, HES-SO Valais-Wallis
3 May 2019 · 11:30 a.m.
Q&A - Dr Vincent Andrearczyk
Dr Vincent Andrearczyk, HES-SO Valais-Wallis
3 May 2019 · 11:48 a.m.
Interpretable models of robot motion learned from few demonstrations
Dr Sylvain Calinon, Idiap Research Institute
3 May 2019 · 11:50 a.m.
Q&A - Sylvain Calinon
Dr Sylvain Calinon, Idiap Research Institute
3 May 2019 · 12:06 p.m.
The HyperBagGraph DataEdron: An Enriched Browsing Experience of Scientific Publication Databa
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Improving robustness to build more interpretable classifiers
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Q&A - Seyed Moosavi
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3 May 2019 · 12:34 p.m.