Nonequilibrium diffusions for density estimation on path spaces

摘要

Many machine perception problems of interest involve continuous valued quantities with continuous time dynamics, e.g., visual tracking. Hidden Markov models, flexible highly parameterized density estimation architectures, are recognized as the state of the art for difficult machine perception problems such as speech recognition. However, the standard HMM arcitecture uses discrete time dynamics and discrete valued hidden states.