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Found 7 papers, 5 papers with code
Towards Unbiased Evaluation of Detecting Unanswerable Questions in EHRSQL
Incorporating unanswerable questions into EHR QA systems is crucial for testing the trustworthiness of a system, as providing non-existent responses can mislead doctors in their diagnoses.
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective.
Re-thinking Federated Active Learning based on Inter-class Diversity
In this work, we first demonstrate that the superiority of two selector models depends on the global and local inter-class diversity.
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
Cell segmentation is a fundamental task for computational biology analysis.
FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning
Robustness is becoming another important challenge of federated learning in that the data collection process in each client is naturally accompanied by noisy labels.
FedBABU: Toward Enhanced Representation for Federated Image Classification
Based on this observation, we propose a novel federated learning algorithm, coined FedBABU, which only updates the body of the model during federated training (i. e., the head is randomly initialized and never updated), and the head is fine-tuned for personalization during the evaluation process.
FedBABU: Towards Enhanced Representation for Federated Image Classification
Based on this observation, we propose a novel federated learning algorithm, coined FedBABU, which only updates the body of the model during federated training (i. e., the head is randomly initialized and never updated), and the head is fine-tuned for personalization during the evaluation process.
