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A Physics inspired Functional Operator for Model Uncertainty Quantification in the RKHS
Rishabh Singh, Jose C. Principe
Accurate uncertainty quantification of model predictions is a crucial problem in machine learning. Existing Bayesian methods, being highly iterative, are expensive to implement and…
Deep Geospatial Interpolation Networks
Sumit Kumar Varshney, Jeetu Kumar, Aditya Tiwari +3
Interpolation in Spatio-temporal data has applications in various domains such as climate, transportation, and mining. Spatio-Temporal interpolation is highly challenging due to th…
SpreadsheetCoder: Formula Prediction from Semi-structured Context
Xinyun Chen, Petros Maniatis, Rishabh Singh +4
Spreadsheet formula prediction has been an important program synthesis problem with many real-world applications. Previous works typically utilize input-output examples as the spec…
Image to Image Translation : Generating maps from satellite images
Vaishali Ingale, Rishabh Singh, Pragati Patwal
Generation of maps from satellite images is conventionally done by a range of tools. Maps became an important part of life whose conversion from satellite images may be a bit expen…
A Kernel Framework to Quantify a Model's Local Predictive Uncertainty under Data Distributional Shifts
Rishabh Singh, Jose C. Principe
Traditional Bayesian approaches for model uncertainty quantification rely on notoriously difficult processes of marginalization over each network parameter to estimate its probabil…