 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 .
<meaning-changed>  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 .
 Moreover, we show that under a certain restriction the curse of dimensionality can be avoided in the approximations by sparse-grid sampling recovery based on the Faber series .