REVIEW 2 cited by
PyRelationAL: a python library for active learning research and development
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Active learning (AL) is a sub-field of ML focused on the development of methods to iteratively and economically acquire data by strategically querying new data points that are the most useful for a particular task. Here, we introduce PyRelationAL, an open source library for AL research. We describe a modular toolkit based around a two step design methodology for composing pool-based active learning strategies applicable to both single-acquisition and batch-acquisition strategies. This framework allows for the mathematical and practical specification of a broad number of existing and novel strategies under a consistent programming model and abstraction. Furthermore, we incorporate datasets and active learning tasks applicable to them to simplify comparative evaluation and benchmarking, along with an initial group of benchmarks across datasets included in this library. The toolkit is compatible with existing ML frameworks. PyRelationAL is maintained using modern software engineering practices -- with an inclusive contributor code of conduct -- to promote long term library quality and utilisation. PyRelationAL is available under a permissive Apache licence on PyPi and at https://github.com/RelationRx/pyrelational.
Forward citations
Cited by 2 Pith papers
-
No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biology
A semi-mechanistic model of CRISPR perturbation screens, built from editing, media, and waiting operations, improves gene-expression prediction when trained with an extra steady-state constraint.
-
When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff
An active-learning method using a bias-variance decomposition and a 'cobias-covariance' matrix with eigendecomposition-based batch selection outperforms BALD and least confidence on synthetic noise benchmarks.
Discussion (0). Continue with ORCID to comment.