Have posted about this before, but I have been working on my Python library called "compactem". A way to make traditional ML models smaller: [1]. * the core id

Have posted about this before, but I have been working on my Python library called "compactem". A way to make traditional ML models smaller: [1]. * the core idea is to selectively use training instances to learn a compact versions of a model. Multiple models are produced with different tradeoffs between accuracy and size. The user is then free to pick one that fits his needs. See [2], [3]. * one can also find the "minimal dataset" a models needs to reach a certain accuracy - [4]. Currently working on: * I am reformulating the problem to work with (twice) differentiable losses to speed-up the compaction process. * Also, I am looking multiple "stages" of compaction to see if that leads to models with further reduced sizes. Refs: [1] https://compactem.readthedocs.io/en/latest/ https://compactem.readthedocs.io/en/latest/ [2] see how the model's generalization is affected, also has accuracy vs size curves: https://compactem.readthedocs.io/en/latest/key_ideas.html#what-to-expect https://compactem.readthedocs.io/en/latest/key_ideas.html#wh... [3] Compaction profile - a plot to visualize the range of size reductions between the original and compacted models: https://compactem.readthedocs.io/en/latest/usage.html#compaction-profile https://compactem.readthedocs.io/en/latest/usage.html#compac... [4] https://compactem.readthedocs.io/en/latest/usage.html#optimal-sample https://compactem.readthedocs.io/en/latest/usage.html#optima...