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Found 6 papers, 2 papers with code
Studying Large Language Model Behaviors Under Realistic Knowledge Conflicts
In cases where the models still fail to update their answers, we find a parametric bias: the incorrect parametric answer appearing in context makes the knowledge update likelier to fail.
Do Deep Neural Network Solutions Form a Star Domain?
This means that two independent solutions can be connected by a linear path with low loss, given one of them is appropriately permuted.
Mitigating Biases with Diverse Ensembles and Diffusion Models
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to a phenomenon known as shortcut learning, where a model relies on erroneous, easy-to-learn cues while ignoring reliable ones.
Trustworthy Machine Learning
Collectively, we face a trustworthiness issue with the current machine learning technology.
Out-of-Distribution Generalization
Uncertainty Quantification
Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to shortcut learning phenomena, where a model may rely on erroneous, easy-to-learn, cues while ignoring reliable ones.
Robust positioning of drones for land use monitoring in strong terrain relief using vision-based navigation
For land use monitoring, the main problems are robust positioning in urban canyons and strong terrain reliefs with the use of GPS system only.
