Background: Scientific workflows improve the process of scientific experiments by making computations explicit, underscoring data flow, and emphasizing the participation of humans...
This work proposes a novel practical and general-purpose lossless compression algorithm named Neural Markovian Predictive Compression (NMPC), based on a novel combination of Bayesi...
Techniques for learning from data typically require data to be in standard form. Measurements must be encoded in a numerical format such as binary true-or-false features, numerica...
V. Seshadri, Raguram Sasisekharan, Sholom M. Weiss
Leveraging the power of nowadays graphics processing units for robust power grid simulation remains a challenging task. Existing preconditioned iterative methods that require inco...
Methods for imputation of missing data in the so-called least-squares approximation approach, a non-parametric computationally efficient multidimensional technique, are experiment...