Search Results for author:
Found 4 papers, 3 papers with code
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite
To this end, we study low-resource generative conversational query rewrite that is robust to both noise and language style shift.
Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning
Our extensive experiments on real-world data also validate the consistent effectiveness, efficiency, and rationale of AMFormer, suggesting it has established a strong inductive bias for deep learning on tabular data.
Robust Image Ordinal Regression with Controllable Image Generation
Image ordinal regression has been mainly studied along the line of exploiting the order of categories.
Robust Training of Graph Neural Networks via Noise Governance
To address these issues, we propose a novel RTGNN (Robust Training of Graph Neural Networks via Noise Governance) framework that achieves better robustness by learning to explicitly govern label noise.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
