Search Results for author:
Found 5 papers, 1 papers with code
The BRAVO Semantic Segmentation Challenge Results in UNCV2024
We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios.
Uncertainty estimation in Deep Learning for Panoptic segmentation
As deep learning-based computer vision algorithms continue to advance the state of the art, their robustness to real-world data continues to be an issue, making it difficult to bring an algorithm from the lab to the real world.
Adjoint-aided inference of Gaussian process driven differential equations
In this paper we show how the adjoint of a linear system can be used to efficiently infer forcing functions modelled as GPs, using a truncated basis expansion of the GP kernel.
Amanuensis: The Programmer's Apprentice
This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018.
How Does Twitter User Behavior Vary Across Demographic Groups?
Demographically-tagged social media messages are a common source of data for computational social science.
