Marc Finzi | DeepAI

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∙ 09/06/2023

CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra

Many areas of machine learning and science involve large linear algebra ...

0 Andres Potapczynski, et al. ∙

∙ 06/13/2023

User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems

Diffusion models are a class of probabilistic generative models that hav...

0 Marc Finzi, et al. ∙

∙ 04/28/2023

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

Unlike conventional grid and mesh based methods for solving partial diff...

0 Marc Finzi, et al. ∙

∙ 04/11/2023

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

No free lunch theorems for supervised learning state that no learner can...

0 Micah Goldblum, et al. ∙

∙ 11/24/2022

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

While there has been progress in developing non-vacuous generalization b...

0 Sanae Lotfi, et al. ∙

∙ 10/06/2022

The Lie Derivative for Measuring Learned Equivariance

Equivariance guarantees that a model's predictions capture key symmetrie...

0 Nate Gruver, et al. ∙

∙ 02/10/2022

Deconstructing the Inductive Biases of Hamiltonian Neural Networks

Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangia...

0 Nate Gruver, et al. ∙

∙ 12/02/2021

Residual Pathway Priors for Soft Equivariance Constraints

There is often a trade-off between building deep learning systems that a...

0 Marc Finzi, et al. ∙

∙ 06/12/2021

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

State-of-the-art methods for scalable Gaussian processes use iterative a...

0 Sanyam Kapoor, et al. ∙

∙ 04/19/2021

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

Symmetries and equivariance are fundamental to the generalization of neu...

0 Marc Finzi, et al. ∙

∙ 10/26/2020

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints

Reasoning about the physical world requires models that are endowed with...

0 Marc Finzi, et al. ∙

∙ 10/22/2020

Learning Invariances in Neural Networks

Invariances to translations have imbued convolutional neural networks wi...

0 Gregory Benton, et al. ∙

∙ 10/21/2020

Probabilistic Numeric Convolutional Neural Networks

Continuous input signals like images and time series that are irregularl...

0 Marc Finzi, et al. ∙

∙ 02/25/2020

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data

The translation equivariance of convolutional layers enables convolution...

11 Marc Finzi, et al. ∙

∙ 12/30/2019

Semi-Supervised Learning with Normalizing Flows

Normalizing flows transform a latent distribution through an invertible ...

13 Pavel Izmailov, et al. ∙

∙ 06/14/2018

Improving Consistency-Based Semi-Supervised Learning with Weight Averaging

Recent advances in deep unsupervised learning have renewed interest in s...

0 Ben Athiwaratkun, et al. ∙

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