1 code implementation • 24 Apr 2024 • Evgenii Kortukov, Alexander Rubinstein, Elisa Nguyen, Seong Joon Oh
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.
1 code implementation • 12 Mar 2024 • Ankit Sonthalia, Alexander Rubinstein, Ehsan Abbasnejad, Seong Joon Oh
This means that two independent solutions can be connected by a linear path with low loss, given one of them is appropriately permuted.
no code implementations • 23 Nov 2023 • Luca Scimeca, Alexander Rubinstein, Damien Teney, Seong Joon Oh, Armand Mihai Nicolicioiu, Yoshua Bengio
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.
no code implementations • 12 Oct 2023 • Bálint Mucsányi, Michael Kirchhof, Elisa Nguyen, Alexander Rubinstein, Seong Joon Oh
Collectively, we face a trustworthiness issue with the current machine learning technology.
Out-of-Distribution Generalization Uncertainty Quantification
no code implementations • 3 Oct 2023 • Luca Scimeca, Alexander Rubinstein, Armand Mihai Nicolicioiu, Damien Teney, Yoshua Bengio
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.
no code implementations • 20 Feb 2018 • Oleg Kupervasser, Vitalii Sarychev, Alexander Rubinstein, Roman Yavich
For land use monitoring, the main problems are robust positioning in urban canyons and strong terrain reliefs with the use of GPS system only.