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02/27/2023
Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts
In reliable decision-making systems based on machine learning, models ha...
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02/10/2023
Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation
The development of automatic segmentation techniques for medical imaging...
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11/09/2022
Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis
This paper focuses on the uncertainty estimation for white matter lesion...
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06/30/2022
Shifts 2.0: Extending The Dataset of Real Distributional Shifts
Distributional shift, or the mismatch between training and deployment da...
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09/13/2021
Multi-Sentence Resampling: A Simple Approach to Alleviate Dataset Length Bias and Beam-Search Degradation
Neural Machine Translation (NMT) is known to suffer from a beam-search p...
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09/09/2021
Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance
The ability to identify and resolve uncertainty is crucial for the robus...
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07/15/2021
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
There has been significant research done on developing methods for impro...
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06/29/2021
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay
Despite the conventional wisdom that using batch normalization with weig...
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05/14/2021
Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets
Ensembles of machine learning models yield improved system performance a...
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06/20/2020
Regression Prior Networks
Prior Networks are a recently developed class of models which yield inte...
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06/18/2020
Uncertainty in Gradient Boosting via Ensembles
Gradient boosting is a powerful machine learning technique that is parti...
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02/18/2020
Uncertainty in Structured Prediction
Uncertainty estimation is important for ensuring safety and robustness o...
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05/31/2019
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
Ensemble approaches for uncertainty estimation have recently been applie...
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04/30/2019
Ensemble Distribution Distillation
Ensemble of Neural Network (NN) models are known to yield improvements i...
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12/06/2018
Prior Networks for Detection of Adversarial Attacks
Adversarial examples are considered a serious issue for safety critical ...
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02/28/2018