  Representations are fundamental to artificial intelligence. The performance
of a learning system depends on the type of representation used for
representing the data. Typically, these representations are hand-engineered
using domain knowledge. More recently, the trend is to learn these
representations through stochastic gradient descent in multi-layer neural
networks, which is called backprop. Learning the representations directly from
the incoming data stream reduces the human labour involved in designing a
learning system. More importantly, this allows in scaling of a learning system
for difficult tasks. In this paper, we introduce a new incremental learning
algorithm called crossprop, which learns incoming weights of hidden units based
on the meta-gradient descent approach, that was previously introduced by Sutton
(1992) and Schraudolph (1999) for learning step-sizes. The final update
equation introduces an additional memory parameter for each of these weights
and generalizes the backprop update equation. From our experiments, we show
that crossprop learns and reuses its feature representation while tackling new
and unseen tasks whereas backprop relearns a new feature representation.
