Reviewer 1: "The algorithm is rather similar to the denoising algorithm referred to in the paper. One could argue that the prediction time17 iswhat really matters. The preprocessing time (including tuning) is at the order of 4,00028 seconds, compared tohundred seconds using ourmethod. Interms ofprediction time, thebest speedup (x8) comes29 at the cost of the worst accuracy(2 times our regret) while the speedup with their best accuracyis only 2to 3folds30 (compared to10folds inourmethods). Perhaps some discussion onunderstanding how much49 training data we could get away with (and whether we could just ignore a lot of the data to save on computation)50 could behelpful..." Indeed, evenXueandKpotufe (2018) suggested thatasmall number ofsubsamples (repititions)51 wouldsuffice.

It works both in the stochastic and the deterministic settings, without hurting the algorithm'sperformance. As applications, our reduction turns Natasha2 into a first-order method without hurting its theoretical performance. It also converts SGD, GD, SCSG, and SVRG into algorithms finding approximate local minima, outperforming some bestknownresults.

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We investigate a sequential optimization procedure to minimize the empiricalrisk functional fbθ(x) = 12kGbθ(x) yk2 for certain families of deep networks Gθ(x).

This consistency ensures perceptual realism and immersion, enriching the overall user experience.

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There are a few simple (and inexpensive) ways to get going. Breakthroughs, discoveries, and DIY tips sent six days a week. This year, the Federal Reserve will begin officially "phasing out" the penny . The plucky little copper-colored coin used to be able to fetch you a piece of candy from the corner store. Then it was found mostly tucked into loafers and floating at the bottom of wishing wells and fountains.

In speech separation, time-domain approaches have successfully replaced the time-frequency domain with latent sequence feature from a learnable encoder.

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