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+incorporating+spatial+information+to+tackle+the+opaque+parameterization+process+of+random+weighted+neural+networks.+The+INN+leverages+spatial+information+to+elucidate+the+connection+between+parameters+and+network+residuals.+Furthermore%252C+it+devises+a+geometric+relationship+strategy+using+a+pool+of+candidate+nodes+and+established+relationships+to+select+node+parameters+conducive+to+network+convergence.+Additionally%252C+a+lightweight+version+of+INN+tailored+for+large-scale+data+modeling+tasks+is+proposed.+The+paper+also+showcases+the+infinite+approximation+property+of+INN.+Experimental+findings+on+various+benchmark+datasets+and+real-world+industrial+cases+demonstrate+INN%27s+superiority+over+other+neural+networks+of+the+same+type+in+terms+of+modeling+speed%252C+accuracy%252C+and+network+structure.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.4617c396e7b76259ec5631eae79576f7%3frik%3dc%252b7qje541ES1WQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Interpretable+Neural+Network&FORM=IRPRST&ck=F5ADFF69C03D53F38620708ED6A93322&selectedIndex=29&itb=0)
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