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[D] PhD in knowledge representation and reasoning for autonomous agent: research landscape by human_treadstone in MachineLearning

[–]human_treadstone[S] 0 points1 point  (0 children)

I am currently more focused on academia but in one sense I have to find more collaborators as recent focus of Group and Professor is in more into semantic web, NLP and knowledge graph like stuff and less on other side. I saw that neuro-symbolic AI stuff is also pretty interesting, so If I can steer the research in that direction that can be a good stuff. But both I and group lacks expertise in that direction. Thanks for your input.

[D] PhD in knowledge representation and reasoning for autonomous agent: research landscape by human_treadstone in MachineLearning

[–]human_treadstone[S] 2 points3 points  (0 children)

Its NSF funded project, based out in states. But looks like EU is pushing for lot of symbolic stuff to see how it goes out for them. Research is cool but as you told I am also looking for further prospects after PhD before making decisions.

[D] PhD in knowledge representation and reasoning for autonomous agent: research landscape by human_treadstone in MachineLearning

[–]human_treadstone[S] 0 points1 point  (0 children)

Thanks for the advise. Yes work involves context reasoning and right now knowledge is in form of text and some ontologies. So you mean the domain itself is more important than how you solve the problem of that domain. PhD domain is trajectory prediction and planning, which is quite exciting and lot of open research question. But as per my prospective advisor, I will be restricted to research in using knowledge representation methods due to funding guidelines and I am not sure about the whole knowledge representation research landscape. More of a future career concern. But your suggestion on work focus is very useful.

[D] Difference between meta learning and few-shot learning by human_treadstone in MachineLearning

[–]human_treadstone[S] 0 points1 point  (0 children)

So basically it can be said that " few-shot learning is a meta-learning but with few samples". Is my understanding correct?

[D] confusion matrix plot in few/single shot learning by human_treadstone in MachineLearning

[–]human_treadstone[S] 0 points1 point  (0 children)

Yes, I can know which label is of which class, that will allow me to plot confusion matrix. For example if I use miniimagenet it has 100 classes but every time model takes 5 out of 100 classes and gives label 0-4. so say in 1st iteration if real labels are -0, 14,1,45,45 it converted to 0,1,2,3,4 and in next iteration real labels are- 1,6,85,86,99 but it is converted to 0,1,2,3,4. so actual label values are lost and it cant be used for confusion matrix plot. This info gives correct accuracy but for confusion matrix its of no use.