1 code implementation • 21 Apr 2021 • Utkarsh Sarawgi, Rishab Khincha, Wazeer Zulfikar, Satrajit Ghosh, Pattie Maes
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.
no code implementations • 19 Dec 2020 • Rishab Khincha, Soundarya Krishnan, Tirtharaj Dash, Lovekesh Vig, Ashwin Srinivasan
In this paper, deep neural networks are used to extract domain-specific features(morphological features like ground-glass opacity and disease indications like pneumonia) directly from the image data.
1 code implementation • 19 Nov 2020 • Rishab Khincha, Utkarsh Sarawgi, Wazeer Zulfikar, Pattie Maes
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.
1 code implementation • 3 Oct 2020 • Utkarsh Sarawgi, Wazeer Zulfikar, Rishab Khincha, Pattie Maes
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.
1 code implementation • 25 Sep 2020 • Utkarsh Sarawgi, Wazeer Zulfikar, Rishab Khincha, Pattie Maes
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.