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Found 9 papers, 2 papers with code
Trinity: Syncretizing Multi-/Long-tail/Long-term Interests All in One
Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e. g. multi-interest, long-tail interest and long-term interest) have been investigated in many existing works.
The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation
To accommodate these scenarios, we consider a new setting dubbed as multiple treatments and multiple outcomes.
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases
Recent studies reveal that various biases exist in different NLP tasks, and over-reliance on biases results in models' poor generalization ability and low adversarial robustness.
Biofilms as self-shaping growing nematics
Active nematics are the nonequilibrium analog of passive liquid crystals in which anisotropic units consume free energy to drive emergent behavior.
Flow Rate Independent Multiscale Liquid Biopsy for Precision Oncology
Immunoaffinity-based liquid biopsies of circulating tumor cells (CTCs) hold great promise for cancer management, but typically suffer from low throughput, relative complexity and post-processing limitations.
Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation
In this study, we focus on the spurious correlation between word features and labels that models learn from the biased data distribution of training data.
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models
For this purpose, we create a Chinese dataset namely DuQM which contains natural questions with linguistic perturbations to evaluate the robustness of question matching models.
An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as An Example
In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of proposed model are presented.
Cost-aware Cascading Bandits
For the online setting, we propose a Cost-aware Cas- cading Upper Confidence Bound (CC-UCB) algo- rithm, and show that the cumulative regret scales in O(log T ).
