Fusion Encoder Networks
CoRR(2024)
摘要
In this paper we present fusion encoder networks (FENs): a class of
algorithms for creating neural networks that map fixed-length sequences to
outputs. The resulting neural network has only logarithmic depth (alleviating
the degradation of data as it propagates through the network) and can process
sequences in linear time (or in logarithmic time with a linear number of
processors). The crucial property of FENs is that they learn by training a
quasi-linear number of constant-depth neural networks in parallel. The fact
that these networks are constant depth means that backpropagation works well.
We note that currently the performance of FENs is only conjectured as we are
yet to implement them.
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