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Found 7 papers, 4 papers with code
On Feature Learning in the Presence of Spurious Correlations
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 learning problem, such as the image backgrounds when classifying the foregrounds.
The Lie Derivative for Measuring Learned Equivariance
In order to better understand the role of equivariance in recent vision models, we introduce the Lie derivative, a method for measuring equivariance with strong mathematical foundations and minimal hyperparameters.
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization.
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian NNs, dramatically outperform other learned dynamics models by leveraging strong inductive biases.
Adaptive Informative Path Planning with Multimodal Sensing
AIPPMS requires reasoning jointly about the effects of sensing and movement in terms of both energy expended and information gained.
Using Latent Variable Models to Observe Academic Pathways
Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers.
Amanuensis: The Programmer's Apprentice
This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018.
