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Found 6 papers, 4 papers with code
Characterising harmful data sources when constructing multi-fidelity surrogate models
These techniques are highly relevant when assessing the performance of a particular design carries a high cost, as the overall cost can be mitigated via the construction of a model to be queried in lieu of the available high-cost source.
Comprehensive Algorithm Portfolio Evaluation using Item Response Theory
Item Response Theory (IRT) has been proposed within the field of Educational Psychometrics to assess student ability as well as test question difficulty and discrimination power.
An Efficient Transformer for Simultaneous Learning of BEV and Lane Representations in 3D Lane Detection
Our method obtains 2D and 3D lane predictions by applying the lane features to the image-view and BEV features, respectively.
Generating Dynamic Kernels via Transformers for Lane Detection
While such methods reduce the reliance on specific knowledge, the kernels computed from the key locations fail to capture the lane line's global structure due to its long and thin structure, leading to inaccurate detection of lane lines with complex topologies.
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Lane Detection
on CurveLanes
PyHard: a novel tool for generating hardness embeddings to support data-centric analysis
We show in a COVID prognosis dataset how this analysis supported the identification of pockets of hard observations that challenge ML models and are therefore worth closer inspection, and the delineation of regions of strengths and weaknesses of ML models.
Anomaly Detection in High Dimensional Data
The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation.
