And adapting Louise would be an interesting thing to work on for sure. Why is it called Louise?
I don't have a working hyp_gen.pl anymore I think but it was just a simple search given the atoms to add to a rule and its negations.
You can do cool stuff with s(CASP). See https://swish.swi-prolog.org/p/non-monotonic_ilp.swinb For an example. The potential hypothesis here are pre generated, but you can imagine an algorithm or adapt an existing one…
One difference is that in Machine Learning you must think of data structures and algorithms. i.e. the practical ways to compute a model. How to represent and transform data while building a model. I think this is given…
Check out: http://cplint.lamping.unife.it/example/inference/tile_map.pl https://www.youtube.com/watch?v=wDr7v94G_MU https://books.google.co.uk/books?hl=en&lr=&id=oaxwDwAAQBAJ&o...
One of the interesting things about this take on Prolog vs the Power of Prolog (https://www.metalevel.at/prolog), is that it attempts non-monotonic reasoning. There is still a lot of value in the ideas of abductive and…
The thing is findall/3 is not really in the standard pure part of Prolog. It has the form: findall(Template,Enumerator,Instances), and can be read as: `Instances is the sequence of instances of Template which correspond…
Mendelian randomization is a good technique to start thinking about causality for epidemiological studies. This is a good paper that demonstrates the approach: https://www.nature.com/articles/srep16645 Millard, Louise…
I am in the uk, I just have a normal sim in the watch which I pay £10 a month for.
I now only use a huawei watch 2 with LTE as my only phone. I do not pair it with a smart phone. It allows me to use google maps, call an uber and see my upcoming appointments by syncing with google. But I can't check my…
I like the notion of 'context' for transfer learning. Where context can be parameterized. The idea is that you learn a general model from your available data and you are able to specialise that model to perform well…
I run a youtube channel called 'playing with prolog' https://www.youtube.com/channel/UCfWpIHmy5MEx2p9c_GJrE_g We demo little things that you can do in prolog. Happy to take suggestions for videos :)
And adapting Louise would be an interesting thing to work on for sure. Why is it called Louise?
I don't have a working hyp_gen.pl anymore I think but it was just a simple search given the atoms to add to a rule and its negations.
You can do cool stuff with s(CASP). See https://swish.swi-prolog.org/p/non-monotonic_ilp.swinb For an example. The potential hypothesis here are pre generated, but you can imagine an algorithm or adapt an existing one…
One difference is that in Machine Learning you must think of data structures and algorithms. i.e. the practical ways to compute a model. How to represent and transform data while building a model. I think this is given…
Check out: http://cplint.lamping.unife.it/example/inference/tile_map.pl https://www.youtube.com/watch?v=wDr7v94G_MU https://books.google.co.uk/books?hl=en&lr=&id=oaxwDwAAQBAJ&o...
One of the interesting things about this take on Prolog vs the Power of Prolog (https://www.metalevel.at/prolog), is that it attempts non-monotonic reasoning. There is still a lot of value in the ideas of abductive and…
The thing is findall/3 is not really in the standard pure part of Prolog. It has the form: findall(Template,Enumerator,Instances), and can be read as: `Instances is the sequence of instances of Template which correspond…
Mendelian randomization is a good technique to start thinking about causality for epidemiological studies. This is a good paper that demonstrates the approach: https://www.nature.com/articles/srep16645 Millard, Louise…
I am in the uk, I just have a normal sim in the watch which I pay £10 a month for.
I now only use a huawei watch 2 with LTE as my only phone. I do not pair it with a smart phone. It allows me to use google maps, call an uber and see my upcoming appointments by syncing with google. But I can't check my…
I like the notion of 'context' for transfer learning. Where context can be parameterized. The idea is that you learn a general model from your available data and you are able to specialise that model to perform well…
I run a youtube channel called 'playing with prolog' https://www.youtube.com/channel/UCfWpIHmy5MEx2p9c_GJrE_g We demo little things that you can do in prolog. Happy to take suggestions for videos :)