research
∙
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 ...
share
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
10/06/2022
The Lie Derivative for Measuring Learned Equivariance
Equivariance guarantees that a model's predictions capture key symmetrie...
research
∙
02/10/2022
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangia...
research
∙
12/02/2021
Residual Pathway Priors for Soft Equivariance Constraints
There is often a trade-off between building deep learning systems that a...
research
∙
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...
research
∙
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...
research
∙
10/26/2020
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
Reasoning about the physical world requires models that are endowed with...
research
∙
10/22/2020
Learning Invariances in Neural Networks
Invariances to translations have imbued convolutional neural networks wi...
research
∙
10/21/2020
Probabilistic Numeric Convolutional Neural Networks
Continuous input signals like images and time series that are irregularl...
research
∙
02/25/2020
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
The translation equivariance of convolutional layers enables convolution...
research
∙
12/30/2019
Semi-Supervised Learning with Normalizing Flows
Normalizing flows transform a latent distribution through an invertible ...
research
∙
06/14/2018