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Found 8 papers, 3 papers with code
Approximately Bayes-Optimal Pseudo Label Selection
We derive this selection criterion by proving Bayes optimality of the posterior predictive of pseudo-samples.
What cleaves? Is proteasomal cleavage prediction reaching a ceiling?
While direct identification of proteasomal cleavage \emph{in vitro} is cumbersome and low throughput, it is possible to implicitly infer cleavage events from the termini of MHC-presented epitopes, which can be detected in large amounts thanks to recent advances in high-throughput MHC ligandomics.
Improved proteasomal cleavage prediction with positive-unlabeled learning
Accurate in silico modeling of the antigen processing pathway is crucial to enable personalized epitope vaccine design for cancer.
Joint Debiased Representation and Image Clustering Learning with Self-Supervision
However, existing methods for joint clustering and contrastive learning do not perform well on long-tailed data distributions, as majority classes overwhelm and distort the loss of minority classes, thus preventing meaningful representations to be learned.
Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision
Learning from positive and unlabeled (PU) data is a setting where the learner only has access to positive and unlabeled samples while having no information on negative examples.
Uncertainty-aware Pseudo-label Selection for Positive-Unlabeled Learning
In this work, we thus propose to tackle the issues of imbalanced datasets and model calibration in a PUL setting through an uncertainty-aware pseudo-labeling procedure (PUUPL): by boosting the signal from the minority class, pseudo-labeling expands the labeled dataset with new samples from the unlabeled set, while explicit uncertainty quantification prevents the emergence of harmful confirmation bias leading to increased predictive performance.
Joint Debiased Representation Learning and Imbalanced Data Clustering
We simultaneously train two deep learning models, a deep representation network that captures the data distribution, and a deep clustering network that learns embedded features and performs clustering.
Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
The results corroborate the necessity of including mobility data and showcase the flexibility and interpretability of our approach.
