bethestateattimetandlet F(t) = [ F1(x(t)1;ξ(t)1),, Fn(x(t)n;ξ(t)n) ] > Rn d betheworkergradients attimet. DenoteY(t) andG(t) asthestate(models) andgradients respectively,ofallnodes,fromtimet τmax tot

Feb-11-2026, 18:16:09 GMT–Neural Information Processing Systems 

These steps include hyper-parameter tuning, running deep learning experiments, collecting results, and generatingfigures.