Their hypothesis is that PR is a good alternative to NN No, they are not correct, because they have not compared their model to state of the art NNs. That's ex

Their hypothesis is that PR is a good alternative to NN No, they are not correct, because they have not compared their model to state of the art NNs. That's exactly why I insist they use standard, canonical datasets, such as ImageNet: because it's easy to find well-known state of the art, and compare the models. Google Scholar, last 4 years: "Letter Recognition Dataset": 98 results, "MNIST": 10,000 results, "Imagenet": 18,000 results. They aren't doing a paper on NN vs PR for image classification If they include image classification results, and claim their model compares favorably to NNs, then yes, their paper is, at least in part, on NN vs PR for image classification. I only commented on image classification results because that's the field I'm familiar with. I suspect other results they presented would also be unconvincing to people working in those areas. Will more research arise from this? If they want more research to arise from that, then they should have made sure their results are convincing, or at least promising. As presented in the paper, they are not.