- Maruan Al-Shedivat, A. Wilson, Yunus Saatchi, Zhiting Hu, E. Xing. 2016. Scalable GP-LSTMs with Semi-Stochastic Gradients. Abstract: There exists a vast array of machine learning applications where the underlying datasets are sequential. Applications range from the entirety of robotics, to speech, audio and video processing. While neuralnetwork-based approaches have dealt with the issue of representation learning for sequential data, the important question of modeling and propagating uncertainty across time has rarely been addressed by these models. For a robotics application such as a self-driving car, however, it is not just desirable, but essential to have complete predictive densities for variables of interest. When trying to stay in lane and keep a safe following distance from the vehicle front, knowing the uncertainty associated with lanes and lead vehicles is as important as the point estimates.
