Classical supervised machine learning algorithms focus on the setting where the algorithm has access to a fixed labeled dataset obtained prior to any analysis. In most applications, however, we have…
Abstract: The problem of transfer and domain adaptation is ubiquitous in machine learning and concerns situations where predictive technologies, trained on a given source dataset, have to be transferred to…
IDS.190 - Topics in Bayesian Modeling and Computation Speaker: Brian Patton (Google AI) Abstract: TensorFlow Probability provides a toolkit to enable researchers and practitioners to integrate uncertainty with gradient-based deep…