This paper analyzes the distribution of cycle lengths in turbo decoding and low-density parity check (LDPC) graphs. The properties of such cycles are of significant interest in the context of iterative decoding algorithms which are based on belief propagation or message passing. We estimate the probability that there exist no simple cycles of length less than or equal to k at a randomly chosen ...

This paper analyzes the distribution of cycle lengths in turbo decoding and low-density parity check (LDPC) graphs. The properties of such cycles are of signi cant interest in the context of iterative decoding algorithms which are based on belief propagation or message passing. We estimate the probability that there exist no simple cycles of length less than or equal to k at a randomly chosen n...

This work explores the application of recurrent neural network (RNN) language and translation models during phrasebased decoding. Due to their use of unbounded context, the decoder integration of RNNs is more challenging compared to the integration of feedforward neural models. In this paper, we apply approximations and use caching to enable RNN decoder integration, while requiring reasonable m...