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+provide+a+robust+paradigm+for+Bayesian+deep+learning.+In+DGPs%252C+a+set+of+sparse+integration+locations+called+inducing+points+are+selected+to+approximate+the+posterior+distribution+of+the+model.+This+is+done+to+reduce+computational+complexity+and+improve+model+efficiency.+However%252C+inferring+the+posterior+distribution+of+inducing+points+is+not+straightforward.+Traditional+variational+inference+approaches+to+posterior+approximation+often+lead+to+significant+bias.+To+address+this+issue%252C+we+propose+an+alternative+method+called+Denoising+Diffusion+Variational+Inference+(DDVI)+that+uses+a+denoising+diffusion+stochastic+differential+equation+(SDE)+to+generate+posterior+samples+of+inducing+variables.+We+rely+on+score+matching+methods+for+denoising+diffusion+model+to+approximate+score+functions+with+a+neural+network.+Furthermore%252C+by+combining+classical+mathematical+theory+of+SDEs+with+the+minimization+of+KL+divergence+between+the+approximate+and+true+processes%252C+we+propose+a+novel+explicit+variational+lower+bound+for+the+marginal+likelihood+function+of+DGP.+Through+experiments+on+various+datasets+and+comparisons+with+baseline+methods%252C+we+empirically+demonstrate+the+effectiveness+of+DDVI+for+posterior+inference+of+inducing+points+for+DGP+models.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.9b8b3878737dd1346b96651acd8ead07%3frik%3dVEJj7gMkLmExyA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Sparse+Gaussian+Processes+Deep+Learning&FORM=IRPRST&ck=BA4DFB6C4B5008F4C082CEDF7799438A&selectedIndex=4&itb=0)
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