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_Sometimes_, and people do find features in neural networks by tweaking stuff and seeing how the neurons activate, but in general, no. Any given weight or laye
by empath75 2y ago
_Sometimes_, and people do find features in neural networks by tweaking stuff and seeing how the neurons activate, but in general, no. Any given weight or layer or perceptron or whatever can be reused for multiple purposes and it's extremely difficult to say "this is responsible for that", and if you do find parts of the network responsible for a particular task, you don't know if it's _also_ responsible for something else. Whereas with a decision tree it's pretty simple to trace causality and tweak things without changing unrelated parts of the tree. Changing weights in a neural network leads to unpredictable results.
- tomhallett 2y agoIf a KAN has multiple layers, would tweaking the equations of a KAN be more similar to tweaking the weights in a MLP/NN, or more similar to tweaking a decision tree? EDIT: I gave the above thread (light_hue_1 > empath75 > svboese > empath75) to chatgpt and had it write a question to learn more, and it gave me "How do KAN networks compare to decision trees or neural networks when it comes to tracing causality and making interpretability more accessible, especially in large, complex models?". Either shows me and ai are on the right track, or i'm as dumb as a statistical token guessing machine.... https://imgur.com/3dSNZrG https://imgur.com/3dSNZrG