In Bayesian analysis, the posterior follows from the data and a choice of a prior and a likelihood. These choices may be somewhat subjective and reasonably vary over some range. Thus, we wish to measure…
When attacking the problem of Autonomous Driving, one must take into account strict computational constraints, posed by the desired low cost of sensors and processors, and by the required real-time performance. Specifically, when considering Driving…
Predictive models are finding an increasing number of applications in many industries. As a result, a practical means for trading-off the cost of deploying a model versus its effectiveness is needed. Our work is motivated…
Deep neural networks (DNNs) have led to significant improvements to the accuracy of machine-learning applications. For many problems, such as object classification and object detection, DNNs have led to levels of accuracy that are acceptable…
Deep neural networks and other machine learning models have been transformative for building intelligent systems capable of visual recognition, speech and language understanding. While recent advances have led to progress for machine intelligence applications running…
