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For autonomous agents to successfully operate in the real world, anticipationof future events and states of their environment is a key competence. Thisproblem has been formalized as a sequence extrapolation problem, where a numberof observations are used to predict the sequence into the future. Real-worldscenarios demand a model of uncertainty of such predictions, as predictionsbecome increasingly uncertain -- in particular on long time horizons. Whileimpressive results have been shown on point estimates, scenarios that inducemulti-modal distributions over future sequences remain challenging. Our workaddresses these challenges in a Gaussian Latent Variable model for sequenceprediction. Our core contribution is a "Best of Many" sample objective thatleads to more accurate and more diverse predictions that better capture thetrue variations in real-world sequence data. Beyond our analysis of improvedmodel fit, our models also empirically outperform prior work on three diversetasks ranging from traffic scenes to weather data.

Accurate and Diverse Sampling of Sequences based on a "Best of Many" Sample Objective