… a set-based twostage end-to-end neural subsampling model that … In the first stage, we
efficiently subsample candidate … grained global information using set encoding functions, followed …

… a stochastic subsampling reinforcement learning (SSRL) framework that is capable of locating
the globally optimal path on the indefinite set \({… {C}}_{sub}\) based on the locally optimal to …

… stochastic average pooling, which incorporates Dropout-like stochasticity in pooling. We
describe the properties of stochastic subsampling … , we consider stochastic subsampling that is …

… To take into account all features from the dataset, rows are selected at random at each
iteration: we call this technique stochastic subsampling. Stochastic subsampling reduces the …

… , Sher considered the use of subsamples as a diagnostic tool t … we show how these
subsample values can accurately es … Theorem 2.1 pre showing the validity of subsampling in the …

… We propose an efficient data subsampling approach which significantly reduces both the
computational cost of the algorithm and the memory requirements when parallelizing: see Sect. …

… To overcome this issue, we consider the use of subsampling techniques to compute
stochastic estimates of the objective function, its gradient, and its Hessian. Given a random set S …

… stochastic approximation to the subsampling distribution is presented in Section 2.4. In Section
2.5, the theory is extended in a simple way to subsampling … The use of subsampling in the …

… from the larger training data set at each training cycle. The later … stochastic sub-sampling
model as our base line model for scaling GP to larger (and therefore more interesting) data sets …

… based stochastic approximation method to address this challenge. At each iteration of the
proposed method, a small subsample … under the framework of stochastic approximation. Since …