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+--+a+Hessian-informed+approach+that+applies+to+any+optimizer+such+as+SGD+and+Adam%252C+and+covers+the+Newton-Raphson+method+as+a+sub-case.+Our+method+automatically+and+dynamically+selects+the+learning+rate+that+accelerates+the+convergence%252C+without+the+intensive+tuning+of+the+learning+rate+scheduler.+In+practice%252C+out+method+is+easily+implementable%252C+since+it+only+requires+additional+forward+passes+with+almost+zero+computational+overhead+(in+terms+of+training+time+and+memory+cost)%252C+if+the+overhead+is+amortized+over+many+iterations.+We+present+extensive+experiments+on+language+and+vision+tasks+(e.g.+GPT+and+ResNet)+to+showcase+that+GeN+optimizers+match+the+state-of-the-art+performance%252C+which+was+achieved+with+carefully+tuned+learning+rate+schedulers.+Code+to+be+released+at+url%7bhttps%3a%252F%252Fgithub.com%252FShiyunXu%252FAutoGeN%7d.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.bcd073f5798a18b335026495de6d5967%3frik%3dnrWH1i3IkdOZQw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Gradient+Descent+vs+Newton+Method&FORM=IRPRST&ck=9978857D250BD9D7796991F3E818BCD7&selectedIndex=21&itb=0)
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