3 accepted papers
For challenging state estimation problems arising in domains like vision and robotics, particle-based representations attractively enable temporal reasoning about multiple posterior modes. Particle smoothers offer the potential for more accurate offline data analysis by propagating information both…
Deep generative models like variational autoencoders (VAEs) are widely used for density estimation and dimensionality reduction, but infer latent representations via amortized inference algorithms, which require that all data dimensions are observed. VAEs thus lack a key strength of probabilistic gr…
Particle filters flexibly represent multiple posterior modes nonparametrically, via a collection of weighted samples, but have classically been applied to tracking problems with known dynamics and observation likelihoods. Such generative models may be inaccurate or unavailable for high-dimensional…