#  Michael Freedman (Harvard): What can ML learn from the proof of the Kolmogorov-Arnold theorem? 

 



####  calendar\_today Date and Time 

 **September 10, 2024** 

 04:45PM - 06:00PM EDT 

####  pin\_drop Location 

 **Jefferson 356 and Zoom**  



 

 



 

 Zoom link: <https://harvard.zoom.us/j/779283357?pwd=MitXVm1pYUlJVzZqT3lwV2pCT1ZUQT09>  
Passcode: 657361  
Speaker: Michael Freedman  
Title: What can ML learn from the proof of the Kolmogorov-Arnold theorem?  
Abstract: The Kolmogorov-Arnold representation theorem shows that even very shallow, non-linear neural nets can express general continuous multivariate functions. I will begin by giving a proof. The theorem has often been regarded as "irrelevant" to machine learning because of the unrealistic precision required in its representation of Real numbers. I agree with this criticism but will present another path to ML-relevancy - not of the statement but of the proof.



 

 



 

 

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