It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models.
We show that our human-human dyad data has interesting trends including that interaction forces are non-negligible compared to the force required to accelerate an object and that the beginning of a lateral movement is characterized by distinct torque triggers from the leader of the dyad.
We predict future video frames from complex dynamic scenes, using an invertible neural network as the encoder of a nonlinear dynamic system with latent linear state evolution.
As autonomous agents become more ubiquitous, they will eventually have to reason about the plans of other agents, which is known as theory of mind reasoning.
Intelligent systems sometimes need to infer the probable goals of people, cars, and robots, based on partial observations of their motion.
Autonomous agents must often detect affordances: the set of behaviors enabled by a situation.
We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs.
Probabilistic programming languages allow modelers to specify a stochastic process using syntax that resembles modern programming languages.