An informed autoencoder estimates subject-specific motor unit parameters from surface EMG by reconstructing signals in a latent space that respects physical laws relating parameters to observed signals.
Informed machine learning - a taxonomy and survey of integrating prior knowledge into learning systems.IEEE Transactions on Knowledge and Data Engineering, page 1
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
The FIL Hypothesis claims that inductive biases outperform purely data-driven methods on GPU programming tasks with non-trivial feedback loops.
citing papers explorer
-
Estimation of Motor Unit Parameters from Surface Electromyograms using an Informed Autoencoder
An informed autoencoder estimates subject-specific motor unit parameters from surface EMG by reconstructing signals in a latent space that respects physical laws relating parameters to observed signals.
-
The FIL Hypothesis: Inductive Biases Help with Kernel Engineering
The FIL Hypothesis claims that inductive biases outperform purely data-driven methods on GPU programming tasks with non-trivial feedback loops.