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Found 5 papers, 5 papers with code
Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings
Reliability of machine learning (ML) systems is crucial in safety-critical applications such as healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of ML systems in deployment.
Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks
In this work, we propose a simple yet effective approach that clusters similar input features together using hierarchical clustering and then trains proportionately split neural networks with a joint loss.
Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia
Reliability in Neural Networks (NNs) is crucial in safety-critical applications like healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of NNs in deployment.
Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensembles
Our work further demonstrates its applicability in a multi-modal setting using a benchmark Alzheimer's dataset and also shows how deep split ensembles can highlight hidden modality-specific biases.
Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity
Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83. 3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4. 60 for MMSE score regression.
