Credit Assignment Problem

The limitation of end-to-end loss optimization in deep learning and challenges in attributing learning signals across network components

The discussion highlights that deep learning’s reliance on end-to-end loss optimization may be fundamentally orthogonal to biological intelligence, which might function more like reservoir computing than current neural architectures. While this global optimization is exceptionally powerful, it faces a significant credit assignment problem that often forces models into rigid processes, such as generating complex imagery in a fixed number of steps regardless of the task's inherent difficulty. However, emerging research into variable continuous variables offers a potential middle ground, suggesting a path toward overcoming these architectural limitations by better attributing learning signals across network components.

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Comparing Deep Learning with neuroscience may turn out to be erroneous. They may be orthogonal.

The brain likely has more in common with Reservoir Computing (sans the actual learning algorithm) than Deep Learning.

Deep Learning relies on end to end loss optimization, something which is much more powerful than anything the brain can be doing. But the end-to-end limitation is restricting, credit assignment is a big problem.

Consider how crazy the generative diffusion models are, we generate the output in its entirety with a fixed number of steps - the complexity of the output is irrelevant. If only we could train a model to just use Photoshop directly, but we can't.

Interestingly, there are some attempts at a middle ground where a variable number of continuous variables describe an image: < https://visual-gen.github.io/semanticist/ >

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