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Found 3 papers, 2 papers with code
Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates
In this work, we for the first time examine how well the confidence values of modern driver observation models indeed match the probability of the correct outcome and show that raw neural network-based approaches tend to significantly overestimate their prediction quality.
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction
Dimensionality reduction is crucial both for visualization and preprocessing high dimensional data for machine learning.
Affect-DML: Context-Aware One-Shot Recognition of Human Affect using Deep Metric Learning
Human affect recognition is a well-established research area with numerous applications, e. g., in psychological care, but existing methods assume that all emotions-of-interest are given a priori as annotated training examples.
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