Expressive power of binary and ternary neural networks

We show that deep sparse ReLU networks with ternary weights and deep ReLU networks with binary weights can approximate $\beta$-H\"older functions on $[0,1]^d$. Also, continuous functions on $[0,1]^d$ can be approximated by networks of depth $2$ with binary activation function $\mathds{1}_{[0,1)}$.