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Found 9 papers, 8 papers with code
Sample, estimate, aggregate: A recipe for causal discovery foundation models
Causal discovery, the task of inferring causal structure from data, promises to accelerate scientific research, inform policy making, and more.
Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words
We evaluate the performance of ChannelViT on ImageNet, JUMP-CP (microscopy cell imaging), and So2Sat (satellite imaging).
Contextual Vision Transformers for Robust Representation Learning
Additionally, we introduce a context inference network to predict such tokens on-the-fly, given a batch of samples from the group.
Learning to Split for Automatic Bias Detection
Classifiers are biased when trained on biased datasets.
Learning Stable Classifiers by Transferring Unstable Features
Empirical results demonstrate that our algorithm is able to maintain robustness on the target task for both synthetically generated environments and real-world environments.
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
In this work, we prove that by interpolating the distributions of the correct predictions and the wrong predictions, we can uncover an oracle distribution where the unstable correlation vanishes.
Few-shot Text Classification with Distributional Signatures
In this paper, we explore meta-learning for few-shot text classification.
Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes
We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations.
Deriving Machine Attention from Human Rationales
Attention-based models are successful when trained on large amounts of data.
