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?