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Found 8 papers, 8 papers with code
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
In this paper, we propose a simple but general framework for large-scale linear algebra problems in machine learning, named CoLA (Compositional Linear Algebra).
Simple and Fast Group Robustness by Automatic Feature Reweighting
A major challenge to out-of-distribution generalization is reliance on spurious features -- patterns that are predictive of the class label in the training data distribution, but not causally related to the target.
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks
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.
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization
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.
Low-Precision Arithmetic for Fast Gaussian Processes
Low-precision arithmetic has had a transformative effect on the training of neural networks, reducing computation, memory and energy requirements.
On the Normalizing Constant of the Continuous Categorical Distribution
This family enjoys remarkable mathematical simplicity; its density function resembles that of the Dirichlet distribution, but with a normalizing constant that can be written in closed form using elementary functions only.
Bias-Free Scalable Gaussian Processes via Randomized Truncations
In the case of RFF, we show that the bias-to-variance conversion is indeed a trade-off: the additional variance proves detrimental to optimization.
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
The Gumbel-Softmax is a continuous distribution over the simplex that is often used as a relaxation of discrete distributions.
