Search Results for author: Marc Finzi

Found 15 papers, 14 papers with code

A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks

1 code implementation28 Apr 2023 Marc Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson

Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providing approximate solutions to problems where using classical solvers is difficult or impossible.

The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning

1 code implementation11 Apr 2023 Micah Goldblum, Marc Finzi, Keefer Rowan, Andrew Gordon Wilson

No free lunch theorems for supervised learning state that no learner can solve all problems or that all learners achieve exactly the same accuracy on average over a uniform distribution on learning problems.

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization

1 code implementation24 Nov 2022 Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson

While there has been progress in developing non-vacuous generalization bounds for deep neural networks, these bounds tend to be uninformative about why deep learning works.

Generalization Bounds Transfer Learning

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.

Residual Pathway Priors for Soft Equivariance Constraints

1 code implementation NeurIPS 2021 Marc Finzi, Gregory Benton, Andrew Gordon Wilson

There is often a trade-off between building deep learning systems that are expressive enough to capture the nuances of the reality, and having the right inductive biases for efficient learning.

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes

1 code implementation12 Jun 2021 Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson

State-of-the-art methods for scalable Gaussian processes use iterative algorithms, requiring fast matrix vector multiplies (MVMs) with the covariance kernel.

Gaussian Processes

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups

4 code implementations19 Apr 2021 Marc Finzi, Max Welling, Andrew Gordon Wilson

Symmetries and equivariance are fundamental to the generalization of neural networks on domains such as images, graphs, and point clouds.

Rubik's Cube Translation

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints

1 code implementation NeurIPS 2020 Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson

Reasoning about the physical world requires models that are endowed with the right inductive biases to learn the underlying dynamics.

Learning Invariances in Neural Networks

1 code implementation22 Oct 2020 Gregory Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson

Invariances to translations have imbued convolutional neural networks with powerful generalization properties.

Image Classification Molecular Property Prediction +1

Probabilistic Numeric Convolutional Neural Networks

1 code implementation ICLR 2021 Marc Finzi, Roberto Bondesan, Max Welling

Continuous input signals like images and time series that are irregularly sampled or have missing values are challenging for existing deep learning methods.

Gaussian Processes Time Series Analysis

Semi-Supervised Learning with Normalizing Flows

2 code implementations ICML 2020 Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson

Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood.

Semi-Supervised Image Classification Semi-Supervised Text Classification

There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average

2 code implementations ICLR 2019 Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson

Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters.

Domain Adaptation Semi-Supervised Image Classification

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