Inverse problems arise when one seeks to recover unknown parameters or functions from indirect, noisy observations via a forward model. The Bayesian framework casts this recovery as the updating of a ...
Paper: "Robust Nonparametric Bias-Corrected Inference in the Regression Discontinuity Design", (joint work with Sebastian Calonico and Rocio Titiunik).
How to improve the performance of CNN architectures for inference tasks. How to reduce computing, memory, and bandwidth requirements of next-generation inferencing applications. This article presents ...
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