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Found 7 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.
Towards Task Understanding in Visual Settings
We consider the problem of understanding real world tasks depicted in visual images.
A study on the use of Boundary Equilibrium GAN for Approximate Frontalization of Unconstrained Faces to aid in Surveillance
Specifically, the present work is a comprehensive study on the implementation of an auto-encoder based Boundary Equilibrium GAN (BEGAN) to generate frontal faces using an interpolation of a side view face and its mirrored view.
