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Found 3 papers, 2 papers with code
Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning
Self-supervised representation learning of biological sequence embeddings alleviates computational resource constraints on downstream tasks while circumventing expensive experimental label acquisition.
Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings
In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance on clinical tasks.
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
Understanding if classifiers generalize to out-of-sample datasets is a central problem in machine learning.
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