  When comparing human with artificial intelligence, one major difference is
apparent: Humans can generalize very broadly from sparse data sets because they
are able to recombine and reintegrate data components in compositional manners.
To investigate differences in efficient learning, Joshua B. Tenenbaum and
colleagues developed the character challenge: First an algorithm is trained in
generating handwritten characters. In a next step, one version of a new type of
character is presented. An efficient learning algorithm is expected to be able
to re-generate this new character, to identify similar versions of this
character, to generate new variants of it, and to create completely new
character types. In the past, the character challenge was only met by complex
algorithms that were provided with stochastic primitives. Here, we tackle the
challenge without providing primitives. We apply a minimal recurrent neural
network (RNN) model with one feedforward layer and one LSTM layer and train it
to generate sequential handwritten character trajectories from one-hot encoded
inputs. To manage the re-generation of untrained characters, when presented
with only one example of them, we introduce a one-shot inference mechanism: the
gradient signal is backpropagated to the feedforward layer weights only,
leaving the LSTM layer untouched. We show that our model is able to meet the
character challenge by recombining previously learned dynamic substructures,
which are visible in the hidden LSTM states. Making use of the compositional
abilities of RNNs in this way might be an important step towards bridging the
gap between human and artificial intelligence.
