Tag
This paper identifies sufficient sample sizes for maximum-likelihood support recovery of sparse binary signals in high-SNR regimes, revealing an information-theoretic threshold and trade-offs between measurement sparsity and computational cost.
This paper presents a Bayesian filtering approach to learn Lagrangian dynamics from partial, noisy measurements by parameterizing kinetic and potential energies with neural networks and jointly estimating states and parameters via maximum likelihood.