Search Results for author: Nate Gruver

Found 10 papers, 7 papers with code

Large Language Models Are Zero-Shot Time Series Forecasters

1 code implementation NeurIPS 2023 Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson

By encoding time series as a string of numerical digits, we can frame time series forecasting as next-token prediction in text.

Imputation Time Series +1

On Feature Learning in the Presence of Spurious Correlations

1 code implementation20 Oct 2022 Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson

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

1 code implementation6 Oct 2022 Nate Gruver, Marc Finzi, Micah Goldblum, Andrew Gordon Wilson

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.

Deconstructing the Inductive Biases of Hamiltonian Neural Networks

1 code implementation ICLR 2022 Nate Gruver, Marc Finzi, Samuel Stanton, Andrew Gordon Wilson

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

no code implementations21 Mar 2020 Shushman Choudhury, Nate Gruver, Mykel J. Kochenderfer

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

no code implementations31 May 2019 Nate Gruver, Ali Malik, Brahm Capoor, Chris Piech, Mitchell L. Stevens, Andreas Paepcke

Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers.

General Classification

Amanuensis: The Programmer's Apprentice

no code implementations29 Jun 2018 Thomas Dean, Maurice Chiang, Marcus Gomez, Nate Gruver, Yousef Hindy, Michelle Lam, Peter Lu, Sophia Sanchez, Rohun Saxena, Michael Smith, Lucy Wang, Catherine Wong

This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018.

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