Deepity: A C++ library showing Predictive Coding Networks can match Backprop (97.73% on MNIST in 60s) [P]

[u/Important-Home4431]

I've spent the last month building a local C++ machine learning library called Deepity to test alternative credit assignment algorithms; specifically Predictive Coding Networks (PCNs). While PCNs are fascinating for biological plausibility and continual learning, naive implementations are painfully slow.

By implementing recent research (Accelerated PCNs via Direct Kolen-Pollack Feedback Alignment) and utilizing algorithmic caching to bypass redundant forward projections during the inference settling phase, I managed to close the performance gap with backpropagation on my CPU when training on MNIST (50 epochs).

• PyTorch Backprop (Feedforward): 98.27% test accuracy in ~70s.
• Deepity DKPPCN: 97.73% test accuracy in 59.5s.

Next up is porting these kernels to CUDA to scale up the architecture and testing its capabilities in continual learning scenarios where standard backprop struggles.

If you are interested in local learning, alternative credit assignment, or HPC for ML, I'd love your feedback!

• GitHub: https://github.com/ra4ster/deepity
• Project Site: https://ra4ster.github.io/Deepity

[u/wizard_of_menlo_park]

MNIST is too simple of a benchmark .

[u/RegisteredJustToSay → u/wizard_of_menlo_park]

Isn't it amazing how we now consider it trivial enough to even accidentally obtain good scores on, when it was a legitimately tough dataset at one point in time?

[u/wizard_of_menlo_park → u/RegisteredJustToSay]

Yes, this just shows how much the field has progressed since then.