However, many countries and regions are not prepared for the emergence of this phenomenon, and the limited supply of LMO has resulted in unsatisfied usage needs in many regions.
Traditional AI approaches in customized (personalized) contextual pricing applications assume that the data distribution at the time of online pricing is similar to that observed during training.
We then illustrate that smart charging and minor modifications to flight schedules can substantially reduce peak power demands, and in turn the needs for grid infrastructure upgrade.
We show this notion of pipelined network flow is optimized using network paths that are both short and wide, and develop efficient algorithms to compute such paths for given pairs of nodes and for all-pairs.
We focus on incorporating monotonic trends, and propose a novel gradient-based point-wise loss function for enforcing partial monotonicity with deep neural networks.
Multiple machine learning and prediction models are often used for the same prediction or recommendation task.
We also measure the real-world business impact of these approaches by deploying them in an A/B test on an airline's internet booking website.