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Found 4 papers, 2 papers with code
On the connection between Noise-Contrastive Estimation and Contrastive Divergence
Noise-contrastive estimation (NCE) is a popular method for estimating unnormalised probabilistic models, such as energy-based models, which are effective for modelling complex data distributions.
Active Learning with Weak Supervision for Gaussian Processes
Annotating data for supervised learning can be costly.
Robustness and Reliability When Training With Noisy Labels
We find that strictly proper and robust loss functions both offer asymptotic robustness in accuracy, but neither guarantee that the final model is calibrated.
A general framework for ensemble distribution distillation
Ensembles of neural networks have been shown to give better performance than single networks, both in terms of predictions and uncertainty estimation.
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