Code automorphisms used for data augmentation during training and inference allow syndrome-based neural decoders to closely approach maximum likelihood performance on short high-rate codes with small datasets.
On the design and performance of machine learning based error correcting decoders
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Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding
Code automorphisms used for data augmentation during training and inference allow syndrome-based neural decoders to closely approach maximum likelihood performance on short high-rate codes with small datasets.