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Found 6 papers, 6 papers with code
Autoencoder-based prediction of ICU clinical codes
However, the adversarial autoencoder achieved the best performance when using the codes plus variables (F1=0. 32, MAP=0. 25).
Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes
We investigate different natural language processing (NLP) approaches based on contextualised word representations for the problem of early prediction of lung cancer using free-text patient medical notes of Dutch primary care physicians.
Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes
The combination of these two challenges is particularly relevant since newly emerging classes typically resemble only a tiny fraction of the data, adding to the already skewed class distribution.
Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input Modalities
In conclusion, it is crucial to consider the semantics of the item co-occurrence for the choice of an appropriate recommendation model and carefully decide which metadata to exploit.
Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels
We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation.
Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference
In this setup, we compare adapting pretrained graph neural networks against retraining from scratch.
