Maybe this is too broad to be useful? Like asking “Got ML papers that use weights?”

In the video, Yannic commented on the idea at 27:48 https://youtu.be/Cs_j-oNwGgg?t=1668

I am looking for a (gradient descent) optimization outer loop, iterating over data points, which backpropagates through some optimization inner loop of **several small steps to get to one data point**. Each weight would be used multiple times to predict one data point.

- HTM, I believe,
**uses each weight once per data point**: NO
- Deep learning: only uses each weight once (But across N layers, so could argue it counts… but… ) NO
- Energy Based Models: can perform gradient descent for several iterations just to make 1 prediction. (The above paper does a variation of this): YES
- Meta-reinforcement learning: where there is an outer-outer loop, across tasks, and the traditional gradient descent inside each task. (But no smaller-than-task loop): NO
- “Neural Ordinary differential equations” paper: backpropagate through an ODE solver several times for each data point/prediction: YES

Does AlphaZero count? […] Is it a synonym of “planning / foresight”, or if not how do they relate?

Yes I think that counts, I’ll take a look, thanks! Would you consider “planning/foresight” a specific modeling technique or just an idea?