hlogp
c13d5a10028586fdc15ee7da97b7563f-Supplemental-Conference.pdf
This section reports the recall performance of MHN and BayesPCN models on high query noise associativerecalltasks. Table5describes theCIFAR10 recallresults ofninestructurally identical BayesPCN models with four hidden layers of size 1024, a single particle, and GELU activations but with different values ofσW andσx. Onvisual inspection, we found that the model's auto-associative recall outputs for both observed and unobserved inputs became less blurry asmore datapoints were written into memory. Both GPCN and BayesPCN at the core are as much generative models as they are associative memories.