23 citations · 40 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 23 cited
On Feature Learning in the Presence of Spurious Correlations
Pavel Izmailov, Polina Kirichenko, Nate Gruver +1
Deep classifiers are known to rely on spurious features $\unicode{x2013}$ patterns which are correlated with the target on the training data but not inherently relevant to the lear…
cs.LG2022★ 14 cited
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Nate Gruver, Marc Finzi, Samuel Stanton +1
Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian NNs, dramatically outperform other learned dynamics models by leveraging strong inductive biases. These mo…
cs.LG2019★ 3 cited
Using Latent Variable Models to Observe Academic Pathways
Nate Gruver, Ali Malik, Brahm Capoor +3
Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers. Yet important decisions are…