Momentum Resnet
2 long-form posts on Momentum Resnet: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
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Skip Connections With Inertia, in JAX/Flax NNX
A runnable companion: the residual block as a forward-Euler step, then the momentum residual network as a Flax NNX module with one extra state, a velocity the blocks write into. Train both on the rings task a first-order flow cannot separate exactly, watch the training crystallize, and run the trained network exactly backward until floating point, amplified by 1/mu per layer, steals the past.
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Your Skip Connection Is Half of Newton
A residual block x + F(x) is one forward-Euler step: depth is time, the block is a velocity, position moves directly. That is half of Newtonian mechanics. A planet does not update position from force; force updates velocity, velocity updates position, and that split is why orbits are stable. So what does the missing half cost a deep network? We let the physics make three predictions about trained networks, then check all three live in the page. One of them comes back stranger than we wrote it.