 Moreover, we show that under a certain restriction the curse of dimensionality can be avoided in the approximations by sparse-grid sampling recovery and deep ReLU neural networks .
<clarity>  Moreover, we show that under a certain restriction the curse of dimensionality can be avoided in the approximations by sparse-grid sampling recovery and deep ReLU neural networks .
 The proof of these results are in particular, relied on the approximation by sparse-grid sampling recovery and deep ReLU neural networks .