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Found 11 papers, 6 papers with code
TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering
These results demonstrate that feature ordering can be a viable approach to improved deep learning of tabular data.
Efficient Classification of Histopathology Images
We use images with annotated tumor regions to identify a set of tumor patches and a set of benign patches in a cancerous slide.
Unlearning Information Bottleneck: Machine Unlearning of Systematic Patterns and Biases
Effective adaptation to distribution shifts in training data is pivotal for sustaining robustness in neural networks, especially when removing specific biases or outdated information, a process known as machine unlearning.
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)
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.
