2 papers
cs.LG2025
Probabilistic and nonlinear compressive sensing
Lukas Silvester Barth, Paulo von Petersenn
We present a smooth probabilistic reformulation of regularized regression that does not require Monte Carlo sampling and allows for the computation of exact gradients, fac…
cs.LG2025
Efficient compression of neural networks and datasets
Lukas Silvester Barth, Paulo von Petersenn
Compression and generalization are fundamentally related through Solomonoff induction and the minimum description length principle (MDL), which predict that simpler models generali…