Hardware implementation of Low-Density Parity-Check (LDPC) decoders using conventional algorithms such as Sum-Product or Min-Sum requires large amount of hardware resources. A rather simplistic way to reduce hardware resources is to reduce the intrinsic message quantization. However this adversely affects the bit error rate (BER) performance significantly. In this paper, a resource efficient LD...

In this letter, we study turbo product codes with quadratic residue (called QR-TPCs) as the component codes. We propose an efficient decoder based on Chase-II algorithm two convergence conditions for iterative decoding of QR-TPCs. For each row and column, will stop immediately when one is met. The simulation results show that proposed has a lower computational complexity compared existing metho...

Solving for quantum error correction remains one of the key challenges computing. Traditional decoding methods are limited by computing power and data scale, which restrict efficiency color codes. There many that have been suggested to solve this problem. Machine learning is considered most suitable solutions task code. We project code onto surface code, use deep Q network iteratively train pro...

In this paper we describe MARIE, an N -gram-based stochastic machine translation decoder. It is implemented using a beam search strategy, with distortion (or reordering) capabilities. The underlying translation model is based on an N gram approach, extended to introduce reordering at the phrase level. The search graph structure is designed to perform very accurate comparisons, what allows for a...

This paper describes the CMU submission to shared task 1 of SIGMORPHON 2017. The system is based on the multi-space variational encoder-decoder (MSVED) method of Zhou and Neubig (2017), which employs both continuous and discrete latent variables for the variational encoder-decoder and is trained in a semi-supervised fashion. We discuss some language-specific errors and present result analysis.