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Found 4 papers, 4 papers with code
MTTM: Metamorphic Testing for Textual Content Moderation Software
In addition, we leverage the test cases generated by MTTM to retrain the model we explored, which largely improves model robustness (0% to 5. 9% EFR) while maintaining the accuracy on the original test set.
SSE-PT: Sequential Recommendation Via Personalized Transformer
Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed for better use of the temporal ordering of items that each user has engaged with.
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Temporal Collaborative Ranking Via Personalized Transformer
Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed us to make better use of the temporal ordering of items that each user has engaged with.
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers
We find that when used along with widely-used regularization methods such as weight decay and dropout, our proposed SSE can further reduce overfitting, which often leads to more favorable generalization results.
