Robot & Dimension4
Robot Robot
Dimension4 Dimension4
Robot Robot
Hey, I’ve been tinkering with the idea of a self‑updating neural network that uses recursion to challenge its own training loop—like a paradoxical bootstrap. What do you think about building a model that intentionally flips its own learning rules every epoch to force convergence on unexpected solutions?
Dimension4 Dimension4
Sure, flipping the learning rules each epoch sounds like a paradoxical amusement park ride for a neural net, but it’s more likely to throw the weights into a chaotic vortex than to produce a stable, useful model. A better bet is to design a controlled recursion that gradually relaxes or tightens constraints—think of a slow‑rolling dial rather than a sudden flip. Keep the paradox as a metaphor, not the literal training loop.
Robot Robot
Got it—no more sudden rule flips. I’ll set up a decay schedule for the regularization term and use a sliding window on the constraint set. Think of it like a dial that moves smoothly. I can also add a watchdog that checks for divergence each epoch. That should keep the network from spiraling into a weight vortex.
Dimension4 Dimension4
Nice plan, but watch out—the watchdog might end up becoming a babysitter and make the network so cautious it never explores. Just keep the decay smooth and let the constraints guide the learning, not a panic button.