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you.[Music].so it's my pleasure today to welcome you.chaang who is a machine or sorry ass PhD.student in the machine learning.department at Carnegie Mellon University.he works with advisors Archer dubrovsky.and Archie Singh and has worked on.interactive learning problems as well as.NLP and machine reading comprehension.today he's going to be talking to us.about efficient learning from diverse.sources of imitation so everyone please.welcome you Chong thanks for the.introduction.today I'm going to talk about efficient.learning from diverse sources of.information and let's go so today we are.really living in an era where the area.of machine learning is booming I think.everyone in this room you will have your.favorite machine learning models and the.machine learning applications I'll just.name a few here so for example on the.imagenet recommen is recognition tasks.we have scenes like alex net rest net.outperforming the best human performance.remoras in leon a game of gold we have.alphago between the top human players.and also more reason the natural.language processing task the model bird.has beaten the human performance many.tasks like scored for machine reading.comprehension however a machine learning.is highly inefficient so so one way to.look at it is the number of data to use.so if we look at image net that contains.40 million images and if we look at this.is a machine translation data set it.contains 50 million words from English.to French and if we look at Birth is.trained on the combination of Wikipedia.and book hoppers and it contains 2.5.billion words that's probably the much.more than the number of images we can.see in our whole life or the number of.words in like we can really know that.they are not only inefficient in the.number of data they use they are also.inefficient in their energy use so if we.look at their carbon.this is from a recent blog post if we.train our transformer with neural.architecture search you image 60 times.the cover emission of a human life in.one year so essentially 16 years of.human covering mission so that's a lot.of energy but that's only for training.one model so how can we make machine.learning more efficient how can we make.it use less energy and use less data to.train to look at this problem we should.probably ask ourselves how does human.learn how do we ourselves learn with.limited amount of man energy and limit.the amount of data well there are.certainly many ways to look at this.problem so one way to look at this is.interactive learning so what do I mean.there by that is that in traditional.learning machine learning we learn with.samples and labels so let's say if we.want to learn the image recognition task.we have dog images cat images horse.images and we want to induce a function.from the samples to tables.well that's a way to learn but in human.learning we can learn through more kinds.of interactions so for example we do not.only learn from these examples so sample.table pairs we can also learn from.explanations basically you can look at.maybe definition of a dog on the.Wikipedia you can learn through a.comparisons like you compare a new image.to a dog image and a cat image we can.you can also learn through other forms.of image like other than these photos.like this sketch sketches to learn.better water dog is the reason that we.learn from source more kinds of.interactions it's not only that they are.more accessible to us but they are also.more informative than using the row.labels and on the other hand we can also.combine these multiple kinds of.information to unlearn our target tasks.so that's interactive learning another.way of looking at human learning is the.transfer of knowledge.so this is a code from British and.encyclopedia.human intelligence mental quality that.consists of the abilities to learn from.experience adapt to new situations.understand then handle abstract concepts.and use knowledge to manipulate one's.environment so you can see that the.ability to learn from experience and.adapting to new situations is a core.quality of human intelligence so in this.talk I'm going to talk about learning.from diverse information and be mainly.talking about two areas.the first one is interactive learning.shows diverse types of questions where.we use the diverse type of questions to.get more intuitive and informative.knowledge so we'll be mainly in study.learning from preferences or pairwise.comparisons in this talk and on the.other hand I also talk about multi class.and transfer learning from multiple.domains so that's the outline of this.talk so I'll talk about learning with.preferences I've been many talking about.this working on an parametric route.question with comparisons and I briefly.touched another three works I did on.these similar topics and then I also.talked about multitask and transfer.learning so I'll be talking mainly about.multitask learning for machine.comprehension and then also briefly.about transfer learning for medical.question answering as to the first part.so why do we want to learn from.comparisons so this is a motivating.example so let's consider the problem of.estimating people's ages from their.portraits so for example we have these.images we want to estimate how old are.these people maybe want to say our.closest to them and then the traditional.kind kind of query like we if we want to.cross the data is that we give this.image to a person and then we ask how.old is he so we give an X and we get.back a why other word these kind of.things are not very easy for the.crowdsource worker to judge because like.it's not very easy based on just this.one image and also we find.people tend to have large variance.especially for older people I know.Hannah Hannah you can also another kind.of query basically you give two images.to a person and then you ask who looks.older so since you have a baseline in.this case people will be easier to judge.this kind of query they will have a.better accuracy and they will take less.time in doing such tasks so we give X 1.and X 2 we get back up Z correspond to.FX 1 minus FX 2 so what's special here.so since we are using comparisons can we.use solve the problem using only.comparisons such as we used learning to.rank essentially not because our goal is.to learn regressor we don't need to map.from the images to the to the ages.instead of just ranking their images so.it turns out that we need to combine.both the comparisons and the label.queries to learn a model jointly so in.the in the example here our goal is to.design into interactive algorithm that.can decide which type of data to collect.and when and how much applications of.learning from preferences there are many.so for example estimating image.properties like ages because human are.often better at doing comparisons then.doing direct labels another application.is patient diagnosis where if you want.to use the red labels you have to do.some invasive dangerous and expensive.experiments and under Hannah Hannah you.can obtain some comparisons from the.expert in the doctors but the doctors.can often not give a very good quality.direct labels on the other hand we can.also apply our techniques to material.synthesis where for direct labels you.need real synthesis to get the direct.levels on the other hand you can hurry.the expert to provide comparisons on.their expectations of different.materials in the sis configurations but.they cannot provide good estimate under.the values of the outcome.so this is the outside of this part I'm.going to talk about our algorithm along.with the theoretical analysis and then.lastly about the experiments our problem.setup is in the nonparametric regression.case so that's some pad our message is.actually generally applicable to other.forms of blue question as well so let's.say we want to learn a smooth function.from X to Y and then we have a unlabeled.data pool let's say initially all the.data are not labeled let's say they are.within 0 1 to the D the feature space.here we consider ascending supervised.learning setting with two kinds of.labels.the first one is direct labels or.Cardinal labels so we have n points.let's just say this label or subset of.them subset of n points they are labeled.so x TI y TI for some t1 through TN the.label and the label come as y TI + FX FX.TI plus epsilon I and here epsilon is.and noise it is has some min 0 and the.bounded variance we also have audio.levels basically correspond to.comparisons but here we analyze two.types of audio no labels the first one.is our ranking so let's just say we.initially have a potentially noisy.ranking of Pi head of samples and.whenever PI hat ranks are less than or.equal to J it means FX is less than.equal to FX and we also have comparisons.also twice pair excited and actually we.can query a variable z IG such that the.idea indicates the order of FXI and FX j.and it's correct with the probability of.a plus lambda.there is the assumption that there is a.DS comparisons are consistent with an.underlying score function this.comparison is present so the camera here.doesn't naturally need to because.in case they have to do so the ranking.casually just has didn't we have a.ranking you can obtain the rankings.through comparisons or maybe you trained.on rancor yeah we'll come to this later.so if you if you ask the system twice.about the same pair of inputs yeah so I.wasn't doesn't depend on this so it.you'll never acquire it twice but you.can consider so sorry together yeah any.more questions all right so so how do we.do this problem to give you some.intuition let's first consider the case.of M ecosan and then we have a perfect.ranking so look here.so I explained more detail is that so a.subset of n points are labeled so if M.equals in it means every points are.labeled and if we have a perfect ranking.it means this statement is universally.true for or FX for X I and extract so.without loss of generality since we have.a perfect ranking here let's just assume.F FX 1 is less than equal to FX 2 and.all the way to F xn is not decreasing.and we also have all the labels since we.assume chemicals in y equals FX I plus.epsilon I so the normal way we if we.have all the labels so is that we want.to induce a function from X I to Y I by.learning this regression function but if.we have the ranking we can actually do.better so we can improve the labels you.by using a technique called isotonic.regression so the idea is that we so.note that the Y's might not fit the.order here because it has noise so our.goal here is that we fit a new set of.values Y by 1 hat student whiten hat.where Y I had is less than or equal to.YJ hat for I less than J.basically that means that why I had.sequence is non decreasing and subject.to this will minimize the error between.mean square error between waiawa via hat.and.so this program is a convex optimization.program and there's actually all an.algorithm to automate this thing the.benefit of using this isotonic.regression is that John in 2002 shows.that the mean square error between why I.had an FX I decays as the rate of unto.the miners true sir.so this is independent of whatever F we.have here like it can be discrete or.like in continuous or some anything and.also note that if we use Y I hear Y.minus FXI is epsilon right and this will.be the variance of epsilon so it will be.a constant so the advantage of using.acetone in regression is that we can get.a decaying mean square error on the.labels irrelevant what of whatever if we.have so that's the case of M ecosan and.then let's look at the case of M less.than so if M less than so remember M is.the number of labels we have so if I'm.less less than we will have uh none.labels imposed so let's say YJ is the we.don't know those label when they've.always done known but let's say Y and YK.they are known they are labeled samples.that are closest to YT what we can know.about YJ it turns out that why I am YK.they are nearest neighbors within the.ranking in the label points so why is.the closing nearest neighbor to the left.of YJ and YK is the nearest neighbor to.the right of Y T so we can roughly just.say YJ is roughly why I first Y K.divided by 2 so the observation here is.that the ranking provides information on.the nearest neighbors in the one.dimensional label space so note that the.ranking is just based on the labels it's.not based on the features so here the.nearest neighbor is just on the one.dimensional label space and we just we.don't actually need many label samples.to reach a low error here.so our algorithm is we called a ranking.regression or a square algorithm works.following our intuitions previously.still let's first assume we have our.perfect ranking I'll come to a noisy.ranking later fulbright you is never the.case that you only have a rank you know.some say it's always ranking with the.focuser so so we first ranked a set of.input points as here also may be.indicated by these arrows here we live.all random subsets of size M from T.that's our assumption actually our next.step is that we run the ISO Tonkin.regression on the label point according.to the ranking basically we minimize.this program subject to the ranking and.then we use nearest neighbors to infer.the values here so after this we infer.the values of the unlabeled samples by.their nearest neighbors in the ranking.so now we have some invert values so now.let's look at this we have a space of.samples and each one we have inferred.value so basically we here I can use any.supervised learning algorithm to learn a.mapping from the features to the to the.to the labels in since we are in the.nonparametric case we use nearest.neighbor again for a new point we just.use nearest neighbor in the feature.space as the prediction any questions on.the algorithm ok so so for theoretical.results when we have a perfect monkey.and underlying function is Lipsius we.can show that the mean square error.between the projection and the ground.truth function decays that was the rate.of M to the minus 2/3 plus M to the.minus 2 divided by D and here the M to.the minors to search come from I so Tony.regression step and n to the minus 2.divided by D confront our.staff of using nearest neighbors the for.regression without using compare.comparisons so in the label only case.the MSU rate is M to the minus two.divided by D plus two so this rate is.exponential in D meaning that if you.even if you want to constantly row you.will have to use exponentially large.number of labels on the other hand on.our return um M is the number of labels.require e is polynomial is independent.of D so comparisons leads to a faster.convergence and label of dependency is.not dimensionally not imaginary.dependent essentially we can escape the.so-called curse of dimensionality using.comparisons on the other hand the.dependence on n is the number of ranked.points is still exponential but this is.a better rate and to the minus two.divided by D then the label only case so.still show some power of using.comparisons we can show a complimentary.lower bound that our algorithm is almost.optimal up to log factors and constants.applying only to still a random draw.import so is it only applying to.basically passive schemes or could I be.adaptive in choosing which n points to.apply adaptive I think it applies so in.general active learning doesn't quite.help for regression we prove it for.passive case I think it also applies for.a queue.now previously we just talked about we.have a perfect ranking so now what if we.have a noisy ranking and what if we want.to use comparators.so actually it turns out that the.similar wisdom can be applied to the.noisy ranking case because things like.isotonic regression.they are noise tolerant and if we want.to use comparisons the idea is that we.first induce a ranking from the.comparisons and then we use our.algorithm and here we combine with the.ranking algorithm from River mmmm also.in 2009 to first induce a ranking and.then use the r-square so when we have a.cylinder of the ranking have a era of.new this introduce another term of.square root of new in our upper bound.mean square error and so the new can be.large like if you have a completely new.idea banking it will be one land and.then ruins things like we can use the.cross validation between ranking.regression and traditional nonparametric.question and then we can get a minimum.of these two things here so it's the.number of pairs then the rankings and.the fraction and so on the other hand we.can show a lower bound which is.basically the same thing but with square.of new instead of square root of u so.this has a gap between the upper and.lower bound another hand.so this is our bound in the noisy.ranking case friend proven I'm also.interested in I shows that and our.assumption of the comparisons you can.induce a ranking of error at most one.over n with high probability and it used.n log n comparisons this will be active.in this case so our color II is that we.just apply this one one and then you can.get a rate of M to the minus 2/3 for us.n to the minus 2/3 and unto the half.and the algorithm use n log n.comparisons so the observation here is.that as long as T is larger than or.equal to four you get the same rate as.the perfect ranking case if you use.comparisons our task for experiment our.task here is yes if you knew that the.error depends on the difference between.the values of F so it for instance the.noise is the higher the closer the.values of F part together this is the.case because it's difficult people to.distinguish right objects over a single.what would that affect these parts would.you basically would it help somehow.take this into account starting so I'm.getting a crowd rate so you mean if you.assume something about the nature of the.errors instance yeah but the error is is.is the higher the closer the F so.together would that help somehow not for.this algorithm but in some cases I think.it'll help like you can obtain so.actually all right.ranking error depends on the this new.and in I think in your case physically.we have lowered new in in your case of.in the case of dimension so what about.partial orders that projected to total.order have errors you mean a partial.they're a popular ranking like several.ranking we have many many functions.underlying can only be explained by a.partial order that have projected to a.total order or have no consistent.ranking that explains it.so any multiple dimensional case think.about people's preferences I might have.trade-off between two dimensions that if.one dimension is fixed right then I then.then there is a preference between them.but between them there are.inconsistencies when you project them to.one thing one dimension like your rate.yes but we need to have a target so I.measured the liquor so that the.comparison error.according to the underlying function.what I'm trying to get at is the fact.that that part of the value of the.locality and the neighborhood space is.that when you're comparing things in a.local neighborhood you have consistency.in a ranking when you project it to an.overall total ranking you lose that.consistency yeah that's an interesting.question.maybe that's a good future work but like.ours and generally depends on the.ability that so comparisons even if you.compare two things far away but they are.similar you can't steal obtain a good.quality comparison so that's actually.where the power of comparison comes from.I think in general like at least in the.image estimation class people are have a.like a universal goal okay so our.experiments these are estimating ages.from portraits as I mentioned before our.data set is called upper wheel so the.name comes from it each image has.associated a parent and a biological age.so average is how the people actually.looks and biological age is how the.people actually is so our label let's.just say we want to estimate the.biological age our level will come from.the biological age but it can be limited.maybe because of privacy issues and on.the other hand our ranking seems to be.collect rule may be crossing there will.be based on our parameters so there will.be some bias between the ranking and the.labels we extract a 128 dimensional.features using face net there are around.4 K training samples and test samples R.square used ranking from a parent ages.for our training samples we plot the.curve of mean squared error versus the.number of curve other labels it's.actually nothing by the crossing lately.if they're at leave a message.it's like how certain you ask Mary.Howard this person.yes just like the last 31st I think any.other question.all right so here are the experiment.results we compared to a few label only.based on like five nearest neighbor ten.years neighbors support vector.regression and our own algorithm ranking.regression use five or ten years.neighbors so you can see the red line.here is the five nearest neighbor and.the blue line here is the R square with.five nearest neighbors and the offer.forms the label only baseline and.overall the black line a square with ten.years neighbors all performed all the.baseline methods so as a conclusion by.using pairwise comparisons we can reduce.the effort to learn a regression.function and we can remove the.dependence on D and so that we can.escape the curse of dimensionality so in.addition to this regression with.comparisons of the some other works on.in this area as well like classification.with comparisons they all saw the.optimization as well as the rest holding.banners problem there's a problem in.multi on benches all these comparisons.I'll briefly touch about is seen so we.can also use can also do classification.with comparisons so for example let's.just if we want to do binary.classification and we can compare two.samples and get a more positive one.different than regression where we use.isotonic regression this corresponds to.a binary search and we can also combine.with active learning case in the.classification case and using.comparisons can reduce the label.complexity also to debt of learning or.stress hold function in one dimension so.there's also a lot of improvements is.the set of labels to acquire so it's.actually because active learning helps.this classification and doesn't have and.we can also do optimization with.comparisons like instead of estimating F.entire domain where we do regression all.transition only care about the ultimate.or the maximum of that nesting so we.consider the case of the comparisons are.biased.meaning that so maybe X comparison.doesn't agree with restrain underlying.function f and it has some systematic.bias it's not like just the noise and so.our origin will optimize by first using.comparisons up to the bias and we are.select a high-value region like this the.green region here and then the next step.we use direct labels to just optimize.within the high-value region and we.don't know if we don't have a query in.the low value reading here and we can.show that comparisons can shrink the.search space to a much smaller region.lastly I do this rustling bandit problem.in modern bandits with comparisons so.the problem setting is that given a set.of K arms you can identify the set of.arms which mean larger now given stress.hotel you can consider a case like the.crowd sourcing binary classifications.and each arm is a sample and the tau is.a half so basically a positive and.negative cost and we developed this rank.search algorithm where we iterate.between our ranking and the binary.search so that we don't have to rank.every sample you know our training set.so we show that the pro complexity of.our ranking ranking search is all ok.we're in the pool only setting you need.K log K pools so it's also exponential.improvement what is a.if you can of pairwise comparison how do.you better ranking and each wrong.there was collect follow this way they.are all terms and when you say.complexity is adequate so how many how.many rank in how many comparisons.killing roughly okay locking so it seems.comparisons are cheaper we get up so in.being banded case view you actually get.F directly observations of so this is a.movie on many problem we don't have a.knowing have like you do the reward to.do just some probably comparisons I'll.observe both I can quote select which.one to work so so these are completely.active I can see like first I set up.which type of worry other is comparison.or other is a comparison or two or.direct level and then I said I wish to.to compare home which one's perfect and.when you say K versus K not a do I have.similar dependence also showing up like.is it yeah it's not a plus something.where the lock is not at all dependent.on the gaps or is it still so like this.is a lock a stiff mean all the gaps are.constant and so the lock case like so.you have a total of characters.lock a lock if I if I if I gap was.epsilon then would it just be a small.time swap get sorry one over epsilon.about one writes on a square right okay.yes so you know so in the threshold.abandoned case you you get better.complexity by having both queries that.don't let you observe the reward as well.as queries that you observe the reward.then in the case when you observe only.only the rewards.is that correct so in the vanilla case.you just observe.a sample of the reward when you went on.writing yeah in your case you can choose.by the terms of the reward on a given in.the given round order observer.comparison compared to our rivers yeah.what outweighed previous are chemi Anya.and so in the in the comparison it seems.a bit counterintuitive you see because.the comparison queries they seem since.you want to do select partners that have.a higher mean than a given threshold it.seems counterintuitive that but.including queries that don't give you.this information directly so so the.Assumption here is the comparisons that.cheaper to get and direct labels so if I.use one comparison to get a label or.versus one I use one directly but to get.the using comparisons is cheaper.facility actually only contains the.direct labels Oh something III should.include both here like but actually like.if we use Poe complexity this low.locally and in correspond to a.comparison complexity of roughly the.same thing okay log cake so essentially.like you design remove most of the mass.into comparisons and then in stripper.any other questions all right so that's.finished the first part we have talked.about learning with preferences.nonparametric regression classification.optimizations result in brand new.department a lot of things using.comparisons so that's all she for mind.so let's finish about this learning with.comparisons let's talk about.multitasking transporter.remember that's also a key part of doing.the human intelligence that we mentioned.before so I've talked about multitask.learning for more active applied voice.from machine comprehension and as well.as medical question so before I start so.this is work I did here like in.Microsoft Research.so don't and so before I start I want to.stay like what is the task of machine.reading comprehension so in the machine.comprehension tasks we are given a.passage and a query associated to a to.the passage and the goal is to answer.the query based on the knowledge within.the passage and the answer is usually a.text benefit in the passage so for.example you'll see what causes.precipitation to fall and if you reach.here precipitation falls under of.gravity so the answer is QWERTY here are.two more examples I'm not going to.details here but the answers are both.text bands within the passage a problem.of between this machine reading.comprehension or MRC is that the Mersey.datasets are typically small so Scot.this is the most prevalent and mercy.dataset and it contains 100,000 data.points that's not a small amount but if.we compare to WMT 13 French to English.it contains 2 million our data points if.we compare the number of words scored.actually shares the document with many.questions though it only contains 1.3.million words and as we have mentioned.this before like machine translation has.15 million words and Wikipedia which is.perched training data contains 2.5.billion words so mr Cedeno says are.typically small but our mercy model has.more than 10 million parameters so if.you use pert large it contains 340.million so that's much more than the.number of words in the dataset so how.can we learn efficiently in this case.well previous masters have a previous.work had foreign world proposed many.methods for this like you can use data.augmentation using back translation that.is the ideas that you translate the.freshest passage through French and then.you translate back you can also use.large-scale language model free training.like Elmo like Bert I think there are.more models nowadays but in this part we.start.a different angle as we have mentioned.we use multi-touch learning on multiple.data sets so the idea of using.multi-touch learning for MRC machine.reading comprehension is very simple.basically we want to train a single.model by leveraging MRC datasets across.different or multiple domains so for.example Scott Muse QE and Marco who did.what they are all MRC data set will be.used so the outline is here we first.introduced the model architecture and.then the multi-touch training orbital.and lastly the experiment results our.emphasis is on the multi-touch training.our our model we call it multitask.stochastic answer network or MTN is.similar to the stochastic internal.network of previous paper and actually.our method is agnostic to the Moller.architecture so it's not an emphasis.here it's only seen we adapt or you know.stand is that with all the layers except.the answer layer I shared of course all.data sets now in more detail here's the.model architecture it's actually our.model but I are not going to detail but.basically we have lexical encoding like.word embeddings here and contacts Joe.and Cody this is our esteem and memory.attention which is a transformer and.then we have answer modules we create.answer module for each of the data set.we are going to have we will have spam.based data sets where the answer is a.text span within the passage we will.also have multiple choice data sets.where the choice is we have given a.predefined set of choices and the goal.is to freak from the crib and state our.emphasis on this multi-touch training.algorithm that we developed for MRC we.will first create to talk about some.very basic training algorithm and next.about a multicast learning with a.mixture ritual and lastly multi-touch.learning with sample waiting scheme as.our Maoridom.so the basic algorithm is the most naive.algorithm that you can think about.the digesters we combine all the.datasets into a giant one and we train.on the Uni so in more detail let's say.we have K different data sets and we.divide each data set D K into NK.mini-batches like this and let's let s.be the union of all the mini batches and.then we randomly shuffle F out of 4s so.that we're training in random order and.we train on the sequence of all the mini.batches so although the name no you mess.it all does provide us some gain it.doesn't always work actually most of the.time we only care about the performance.on one particular target data set and we.add the other data sets just for.external data and then we hope to.improve the performance so let's say we.train on Scott let's say our target data.set is called and then if we were.trained on Scott produced q8 you got a.problem with skin like this that's good.but if you train on Scott Plus Marco you.get a very tiny performance key if your.training on combination with Scott frost.nu square plus Marco you get only.actually a worse result then I'll score.+ new screen you can if you're only used.to so the observation here is that the.different data sets helped by a.different amount and adding more data.sets does not necessarily lead to a.better performance so this is probably.because when you train on the.combination of three data sets is.focusing on the Joint Distribution of.the three and it's not optimizing with.respect to Scott this is typically.called negative transfer and we should.try to alleviate this problem well one.easy ways is to just simply down raise.the external data sets so the idea is.that in each iteration we choose an.alpha fraction of external data sets for.training and here offers a hyper.parameter in more detail let's say we.still have K different data sets and we.are targeting the performance and they.deserve more we have this alpha and we.still divide each data set into an k.mini-batches letter SP.first as first be the mini-batches usb1.serbian what and we randomly pick up a.fraction of mini batches from data that.choose or okay and add them to us.although the mixture ratio method where.we search for this alpha can just.alleviate the negative transfer problem.and still has some drawbacks the thing.is that the best performance is only.achievement very specific values of.alpha so if you look at here if we use.our ego 0.4 we get a performance key but.if you use other values we get similar.performance to alpha equals 1 so that's.our but other equals 1 is just a naive.method and the search in this alpha can.be quite tedious especially if you want.to set a separate offer for multiple.data sets so if we look at a problem.more detail you notice that different.questions and answers have different.styles so this is probably due to the.collection process of the data sets for.example if we look at Scott it's based.on Wikipedia and four-cross source.questions the passage is Superbowl 50.was an American football game the game.was played on February 7th 2016 the.question is what day was the game played.on February the 7th 2016 so again notice.that since the crowd questions across is.a full sentence with a question mark and.the answer here is chosen as the.shortest stack span that can answer the.question and so it's February 7th 2016.it's a very succinct and then on the.other hand if we look at a miss Marcal.the questions are actually based on.search queries and the answers are.called so cross host so if we look at a.question.definition of diminution or diminution.it's actually not a full sentence.because when you search you don't.necessarily enter a full question here.the passage is a retrieval result.there's now the definition of a.diminution is reduction in size or.importance when you are demoted this is.an example of a diminution.now the answer here is cross sauce and.it's a full sentence the definition of a.diminution is a reduction in size or.importance so you can see that there are.very different styles if we rewrite the.image Marco in Scottsdale so it will be.what is the definition of a diminution.for sentence and the answer will be a.reduction in size or importance or more.such thing relative to make the problem.more complicated it's not very simple.from ms merkel like this so if we look.at this pair where was moving the birth.field so the user actually dude just the.user actually does enter a full question.and the answer here is a position in.Northern California so it's so short.short succinct answers so both of them.they are similar just cause if you look.at this fully meaning whatever so this.is not a full sentence so the question.is not similar to school but the answer.is a single answer that has a similar.scope so the question in the third pair.is similar to squad what is a.hummingbird moss but the answer here is.very long and probably noisy so it's not.similar to Scott either so our goal here.is to weight the samples according to.their similarity sample it's simulated.to our targeted assess code so that we.only use the useful samples so we use.our we race our algorithm using.multi-touch turning with temporal.weighting scheme that is that we compute.the score as qpa for every training.point question passage answer triplet in.dataset K from K from 2 to through K.reported b1 is our target data set so we.compare computer similarity score s for.every external data points and when we.perform the gradient update we simply.multiply the lost by the similarity.score s qp8 pre-computed so another.person comes to how can we compute this.course here we propose to use language.models to reverse its samples.this is inspired by data selection.techniques in machine translation in.more detail we have K data sets this one.through data set K there's a one is the.target and we train a language model on.each of the data set around once or mmk.the ideas that we would favorite samples.that are similar to data set one but.different from the dessert cake in more.detail for example tqe passage question.answer for this from the other circuit.we use the score as LM one of the.triplet - MK of the triplet so around.one MERIS how similar is the Triple A -.data set one we want this court to be.high because we want similar examples.okay is the similarity score is the.similarity of the triplet to the intern.in domain the data say K Easter no no me.we want this score to be low because we.do not want to buy us our model too much.towards the distribution of the external.data set so as we discuss the major.difference between the data says lies on.the questions and answers and we will.decompose as on one of the question.- arm K of the question + on one of the.answer - MK of towns and this is quite.how we train the language model on the.two forms of data our language model on.the questions we use the.state-of-the-art model on pain treebank.it's a smaller scale language model.covers each model is trained from.scratch on the corresponding data set.questions and we use the cross-entropy.score which is basically the minus log.of the next word prediction on the other.hand for the answers we do not actually.train a language model because the.answers are typically very short and the.mainly contains entity names so that.means that if you train and I'm gonna.follow you and come there are many.unknown words and that's not effective.instead we only use the frequency on the.alternate let offer fre qkb dancer.learns frequency in data set.and we used the minus log of the.frequency of the cross monuments.combining everything together we train.this we have this similarity score by.using the language models and further to.use the scores to rate the loss we.normalize the similarity scores as pqe.to be between the rainbow combining.everything together we reuse the.similarity scores to relate the loss and.lastly is about the experiments we train.for dataset Scott News QE m/s Marco who.did what they are similar size in terms.of the number of training questions.around 100 K but they are different in.their text domains like Wikipedia see in.a news web search and for Scott and miss.Marcal they tend to have a short passage.around 100 word but for news q and who.did what they have much longer passages.and the answer type source code and use.q 8 they use text bands and miss Marco.is the natural language sentence cross.host and who did what is a closed.dataset multiples or multiple choice.where we are given a predefined set of.answers and scrolling news q a they have.short answers MS Marco have a very long.answer and how do you what the answer.here is constrained to be a person's.name so who did those things it will be.just one or two words here are the.results code and use QE based on our.sample rating mrs. Scott if you try on a.single task the ELMO model gives you.eighty six point six and if you use.multi task learning you get a 2.5.performance game on the other hand of.what us QA you guys seem nursing you.train on single task you get 70 point.four and if you train on three tasks you.get a two point four performance key we.mention that our performance.outperformed human performance in terms.of poster exact match.no further than the inner but as well as.the f1 score prettily and here's a.comparison of different D home country.Ottoman teaching methods if you use back.translation it's based on the cue in it.it gives a one point one performance.gain our multitask learning is based on.the stronger model of so Cassie has a.network and it gives a one point five.performance key Elmo gives 2.0 it's more.than multi-touch turning but if we.combine Elmo and as well as multi-touch.learning it gives an additional one.point seven performance game so the.observation here is that the benefits of.multi has learning is more than that.from veteran station and it is also also.the node to the benefits of Elmo after.here's a comparison of different.multi-touch learning strategies for MRC.so the orange bar is the sample relating.method and you can see it performs all.the baselines in both settings and it.alleviates the negative transfer problem.lastly here's some generated scores for.several QE pairs so here here's example.from News QE where's the drought hitting.Argentina is very similar to Scott still.it is done based on similarity with.Scott excused least to a high score and.this sample based on immerse Marco you.can see it's not similar to Scotts so.it's least two zero point two we still.give a positive way to those samples in.general some housing news QE.have a larger weight than samples in.Marco so this fits our intuition that.news QE.can help more than MS Marco and you know.nutshell you know like all the samples.have an average of 0.6 as higher than.the yes always research from mixture if.I were to do a log like your based.which is how likely a micro seed.establish in one versus in a sec - and.away I could get to this log likelihood.is basically by trying to learn up let's.say logistic regression type model which.is trying to tell whether the sample.came from your cell phone or your cell -.they may not ring a classifier yeah.classified and of course condition.problems actually our algorithm is so we.triangular models we do not train a.classifier but the language model is.density estimator so it's it's strange.to take because if you wanted to really.correct the expectations you would take.a ratio of densities yeah so actually I.already so Oh so nobody you take Lexel.how I'm one - so the sample - K of the.sample like this this is the next or.prediction but this will be a very.sparse nothing like because like new.skew is very hard to believe it's P.could be lost to school and it would be.very close to zero and the ratio but.directly learn a conditional probability.estimator for whether the sample came.from data set one versus two and the.important part is that if my conditional.probabilities were calibrated for my.prediction problem then they will give.me the likelihood ratio yeah yeah which.is a trick people have used for your.fancy disk or estimation in yeah I think.yeah yeah it'd still be good - q seems.like yeah I just want to mention one.thing that our message is that actually.like we instead of using this value we.take the log of it and it's basically a.lot of the.- all right we have finished this.multicast learning from machine reading.comprehension perfectly talk about this.transfer learning for medical question.answering so it's actually a competition.that we enter so medic you a medical.question answering 2019 completion the.goal is to do natural language.understanding in medical domain so this.consists of three tasks natural language.inference question atonement and.question now spring so our method is we.call it multi-source transfer learning.so our goal here is natural language.understanding in medical domain and if.we transfer from two sources.with the first one is natural language.understanding in general domain here we.use a crew model mtDNA also develop here.- to transfer and we can also transfer.from language modeling from medical.domain where we use science pert pert.model trainer science tech texts we can.find you in both models on the Anna.medical any of you did have that we have.and our goal is to combine the knowledge.from both models so we mainly use.example a method to combine the combined.knowledge but we can also use knowledge.the situation to combine their.predictions yeah.our fine-tuning process is a multi task.learning process where we use actually.use the mixture ratios with articles put.0.5 so combining the knowledge from TD.and Seyfert we use a majority vote for.the all the tasks and in case of tire.tire Chi result by average scores.not it's distillation using by using the.prediction of Seyfert as the target for.mtDNA but didn't perform quite well in.our experiment so we mostly used and.general method as a result we get the.best performance on the question.answering task in terms of accuracy and.gets a certain natural language.inference and seven song which question.entailment recognize question and here's.a comparison of difference of finger.sauce and Ambo and multi-source and.gamble so these two are single source.and Gamble's so the orange bar are the.average accuracy before in germany and.the gray bar is the accuracy after Emily.so you can notice that single source and.hammering these two have much lower.accuracy than multi-sourcing them so.that finished most contents on my talk.lastly I've got to give an overview of.my PhD research so we've covered many.aspects actually so my research is.centered around learning from diversity.in false covers post learning from.preference with preferences this forcing.we have covered and far as multi-talent.rest for learning of these two I've done.a bit more on learning with preferences.such as linear regression and.comparisons in clean oil trail and.strategy proof conference peer review.where we use reviewers preference on the.papers to induce like what what's the.best paper was the accepted paper set.and I'm also interested in broadly study.skill learning I have developed methods.for active learning for graph neural.networks and quantization techniques for.linear leaving a classification another.interest of mine is merging reading.comprehension.I have develop also in an internship.here and machine reading comprehension.using multiple attention strategies.alright so building upon that so I'm.going to talk about some future works.so one thing that I'm very interesting.is apply this preference based learning.to reinforcement learning the idea is.that in reinforcement learning that we.sometimes have to design design the.reward objectives so for example if we.want to train robots we want to play.games maybe then the naive way of.designing the reward is not very it's.very sparse and it's not very hard to.learn and if we hand design some reward.objectives.there's my not least to the best.performance and then mine may not behave.ideally either so can we can we utilize.human preferences and trajectories in.place of the reward function to better.to better shape it so I'm working on.efficient inspiration I said based on a.policy cover I'm also interested in.optimization wheels or in the case of.linear quadratic regulator case where we.use comparison based optimization to to.do perforin space reinforcement another.point of view is that can we use.preferences to help accelerate the.learning so you know in addition to the.so maybe we can observe the reward but.in addition to that given some policy.and action pairs we can observe the.better one so that we can so similar to.the imitation learnings I think and this.can help the shrink test search space.generally I'm interested in human in the.loop learning so can we use human.intervention to accelerate learning so I.talked about preference learning they.can be also used for things like machine.translation as well so suppose we want.to translate french or english so this.is source sentence in french the.traditional query is that we give this.sentence into to a cross worker and then.we ask what's the translation so the.worker has to type one word by one for.the translation on the other hand we can.also ask comparison queries that we give.to.potential translations and then we just.asked which is better this is much.easier and quicker for human to answer.and but it will be less informative.because it's a binary house in general.I'm interesting in preference learning.for various applications like machine.translation question answering dialog.systems more broadly recommendation.system information retrieval they can.also use preference and user can also.provide rationals suggestions.Corrections on the prediction and I'm.interested in the combination with.reinforcement learning so that is called.reinforced murdering with auxiliary.information I'm interested in the.combination a spell lastly about this.transfer learning that I mentioned so.I'm interesting in transfer learning.from multiple sources nowadays transfer.learning is just many to many mapping we.can transfer more many sources like pert.Seibert Roberta glue knowledge base at.freebase many other talk datasets to our.target is target data like maybe.summarization specific domain and there.you a question answering common sense.reasoning and many more.so which models will transfer from and.which data set which will be find Yuma.so that refers more stati sound effects.a TAS similarities and we can also work.on method learning to select what.sources to transfer from and then maybe.we can use a bandit view of the models.and another question is that how can we.combine knowledge from multiple models.maybe you can use knowledge distillation.and we maybe you can develop.architectures for combine your motives.and thank you that's it.[Applause].so for preference based other types and.I because I was wondering if you've got.any thoughts on how to deal with sort of.what I might call the chorus chart.problem so initially when I again starts.out then basically I think about a.problem and if I ask somebody how are.you great this terrible outcome versus.back there will outcome they're probably.just must crash their heads with no idea.what to say how should I deal with this.so as I mentioned actually we start with.a policy cover so the idea is that you.you try to constrain the.like the branch of the so the branch of.the trajectory you you do it's like so.maybe you make initially you only let.the trajectory branch into like maybe.say in the last time so that so the.human only needs to compare like what.should I do in the last now when you.have done better in the last step you.branch in the second last like this so.it's essentially some kind of demand.then I'm a program based method and so.to do this with initial ATT to use the.policy cover in the sense imitation.learning and universe rate possible.thing could be do this preference based.around since then the human is.demonstrating you a positive it was.Italy they're preferring to to lay males.so could you use that traditionalized.bitterly if you if the human does give.you a demonstration.yeah of course demonstration some much.longer supervision than.references but if you if you were given.a demonstration how would you then use.it to switch to this mode of your.preference.so one ways that we can well first like.we can use the demonstrations like so.the demonstration is a better than.Alexia trajectory generated by our model.so that's one way but so I'm more.targeting about like preference based I.always that like the human doesn't know.what way to do like a maybe like you.want a robot to say fetch a bottle but.the human doesn't know like so you.naturally do this but you don't know how.to teach about the robot so essentially.you are you you want the so instead of.demonstrations where you teach the robot.the best thing to do you also want to.differentiate between like maybe this.time the robot reach out it's better.than not reach out so even amount is.partial partially correct preferences or.partially correct trajectories you also.want the the preferences so that's the.way III will say like preference pacing.reinforcement means like in some cases.it might be more helpful than just you.have the compare the demonstrations if.you had any examples are getting the.analysis of like the types of images for.the engage prediction that your method.works well on versus some of the other.methods which have works free breakfast.fours but in general like a people have.a larger variance other people you can.see that the error is still not very low.like that's mostly the cost of the older.people like a for people of 60 you might.add it and 70 of the t-test very.and a dad questions all right well let's.know their questions let's thank our.speaker one more time okay what I think.[Applause].

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How would we, people of the Earth, react if we were to find out that a highly intelligent life form is somewhere among us and is studying human behaviour?

Most of the other answers seem based on popular ideas that are speculative extensions from our current understanding. As someone who takes this topic very seriously I find these such a hindrance to the topic that I will address them first. Is it valid to say “humans are the only animal who have desig Continue Reading

Why don't schools teach children about taxes and bills and things that they will definitely need to know as adults to get by in life?

Hi, I’m a teacher. Our job as teachers is to teach 24–30 kids how to read, write, Teach fine motor skills(printing,etc.,) Teach gross motor skills, teach science, social studies, and Math. We give uniforms, backpacks and school supplies. We send letters home for Kleenex, hand sanitizers etc. because we don’t get a budget for our classrooms. College would be an appropriate time for taxes. Checking/Savings could be taught way younger. It is not our job to raise your child, this is YOUR child not ours. We do our best for them, and we try to give parents ideas, but we aren’t your child’s parent. I ha Continue Reading

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