We introduce a new method for proving explicit upper bounds on the VC
Dimension of general functional basis networks, and prove as an
application, for the first time, the VC Dimension of analog neural
networks with the sigmoid activation function $\sigma(y)=1/1+e^{-y}$
to ... more >>>

The main result of this paper is a Omega(n^{1/4}) lower bound
on the size of a sigmoidal circuit computing a specific AC^0_2 function.
This is the first lower bound for the computation model of sigmoidal
circuits with unbounded weights. We also give upper and lower bounds for
the ... more >>>

We introduce a new method for proving explicit upper bounds
on the VC Dimension of general functional basis networks,
and prove as an application, for the first time, that the
VC Dimension of analog neural networks with the sigmoidal
activation function $\sigma(y)=1/1+e^{-y}$ ... more >>>