pkoperek

3 ms·

1.
by pkoperek 2y ago
To be honest I think this may be actually an advantage: it explains concepts which otherwise are just weird parameters in code. Since it is pretty lengthy I would actually recommend to read the chapters relevant to a specific method you are
2.
by pkoperek 2y ago
Good read. Does anyone know if any of the experimentation frameworks actually uses these methods to make the results more reliable (e.g. allow to automatically apply winsorization or attempt to make the split sizes even)?
3.
by pkoperek 4y ago
Evolving neural networks with use of the fitness predictors (co-evolution). The project has stalled a bit due to lack of time (family etc) but overall I want to get back hacking around it.
4.
by pkoperek 5y ago
There is one angle I really like in this concept: letting go and acknowledging that something is imperfect rather than continuing to try to make it ideal. I saw so many times people arguing and spending countless hours to reimplement or ref
5.
by pkoperek 5y ago
Genuinely interested - could you share some good resources about it?
6.
by pkoperek 5y ago
Apart from NEAT/HyperNEAT there are also other approaches to neuroevolution (I think in this context it is referred to as "Evolutionary Neural Architecture Search" [0]). Evolution in general can be applied in different ways (
7.
by pkoperek 5y ago
I think the point is that the control group and treatment groups are in fact the same group (you were giving them the same "service" before starting the experiment) and any difference in the beta_C and beta_T is just random noise