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Found 6 papers, 3 papers with code
Bloom Origami Assays: Practical Group Testing
Group testing is a well-studied problem with several appealing solutions, but recent biological studies impose practical constraints for COVID-19 that are incompatible with traditional methods.
Improving Semantic Segmentation via Self-Training
In the case of semantic segmentation, this means that large amounts of pixelwise annotations are required to learn accurate models.
ResNeSt: Split-Attention Networks
It is well known that featuremap attention and multi-path representation are important for visual recognition.
Ranked #8 on
Instance Segmentation
on COCO test-dev
(APM metric)
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file.
Deep Sets
Our main theorem characterizes the permutation invariant functions and provides a family of functions to which any permutation invariant objective function must belong.
Fast and Guaranteed Tensor Decomposition via Sketching
Such tensor contractions are encountered in decomposition methods such as tensor power iterations and alternating least squares.
