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Found 8 papers, 5 papers with code
Towards Independence Criterion in Machine Unlearning of Features and Labels
This work delves into the complexities of machine unlearning in the face of distributional shifts, particularly focusing on the challenges posed by non-uniform feature and label removal.
TimEHR: Image-based Time Series Generation for Electronic Health Records
Time series in Electronic Health Records (EHRs) present unique challenges for generative models, such as irregular sampling, missing values, and high dimensionality.
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
Large language models (LLMs) can potentially democratize access to medical knowledge.
Ranked #1 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
Conditional Text Generation
Multiple Choice Question Answering (MCQA)
MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks
Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space.
Modular Clinical Decision Support Networks (MoDN) -- Updatable, Interpretable, and Portable Predictions for Evolving Clinical Environments
Data-driven Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with personalised probabilistic guidance.
Optimal Model Averaging: Towards Personalized Collaborative Learning
In federated learning, differences in the data or objectives between the participating nodes motivate approaches to train a personalized machine learning model for each node.
WAFFLE: Weighted Averaging for Personalized Federated Learning
Through a series of experiments, we compare our new approach to two recent personalized federated learning methods--Weight Erosion and APFL--as well as two general FL methods--Federated Averaging and SCAFFOLD.
IFedAvg: Interpretable Data-Interoperability for Federated Learning
Thus, enabling the detection of outlier datasets in the federation and also learning the compensation for local data distribution shifts without sharing any original data.
