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Many scientific problems hinge on finding interpretable formulas that fit data, but neural networks are the outright opposite! Check out our recent work that make neural networks modular and interpretable. If you have interesting datasets at hand, we're happy to collaborate!

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Ziming Liu

14,295 subscribers

62,120 Aufrufe • vor 3 Jahren •via X (Twitter)

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Profilbild von Ziming Liu
Ziming Liuvor 3 Jahren

paper: code:

Profilbild von Ziming Liu
Ziming Liuvor 3 Jahren

Symbolic regression (finding symbolic formulas from data) has been quite successful on some examples, but it also fail completely for others. Instead, our goal is structure regression (finding modular structures from data), which can provide visible insights for all cases.

Profilbild von Lei Chen
Lei Chenvor 3 Jahren

Interesting work! Is this doable for graph nets? The task would be to locate significant attributed substructures. (I guess such location might be useful to help predesign the library of GSN @mmbronstein )

Profilbild von ExiledInfoHaz
ExiledInfoHazvor 3 Jahren

👀

Profilbild von Mitchell B. Slapik
Mitchell B. Slapikvor 3 Jahren

Great work Ziming! Could you explain Figure 14? I was a bit confused by it.

Profilbild von Sam McKenzie
Sam McKenzievor 3 Jahren

We suffer in the seizure forecasting field making interpretable models because the features extracted from neural time series are highly correlated. Any tool to understand why our models work would give important insights as to how the brain changes in the lead up to a seizure.

Profilbild von bobert
bobertvor 3 Jahren

I see some resemblance with Group method of data handling (GMDH) .

Profilbild von Zekun Jiang
Zekun Jiangvor 3 Jahren

👍👍👋 nice！

Profilbild von Nick
Nickvor 3 Jahren

it's a clever idea, great job!

Profilbild von Fred
Fredvor 3 Jahren

Can you explain what we are seeing here? Here is my confusion: if you regularize enough, you should see only one layer (either last layer or first layer) activation, the rest is just passing through! Why? Since 1 layer is enough to represent output.

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