28 citations · 92 across the 8 of their papers we have counts for
8 papers
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
Gregory Benton, Wesley J. Maddox, Andrew Gordon Wilson
A broad class of stochastic volatility models are defined by systems of stochastic differential equations. While these models have seen widespread success in domains such as financ…
Bayesian Model Selection, the Marginal Likelihood, and Generalization
Sanae Lotfi, Pavel Izmailov, Gregory Benton +2
How do we compare between hypotheses that are entirely consistent with observations? The marginal likelihood (aka Bayesian evidence), which represents the probability of generating…
Residual Pathway Priors for Soft Equivariance Constraints
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 effici…
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi +1
With a better understanding of the loss surfaces for multilayer networks, we can build more robust and accurate training procedures. Recently it was discovered that independently t…
Learning Invariances in Neural Networks
Gregory Benton, Marc Finzi, Pavel Izmailov +1
Invariances to translations have imbued convolutional neural networks with powerful generalization properties. However, we often do not know a priori what invariances are present i…
Trajectory Based Podcast Recommendation
Greg Benton, Ghazal Fazelnia, Alice Wang +1
Podcast recommendation is a growing area of research that presents new challenges and opportunities. Individuals interact with podcasts in a way that is distinct from most other me…
Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited
Wesley J. Maddox, Gregory Benton, Andrew Gordon Wilson
Neural networks appear to have mysterious generalization properties when using parameter counting as a proxy for complexity. Indeed, neural networks often have many more parameters…
Function-Space Distributions over Kernels
Gregory W. Benton, Wesley J. Maddox, Jayson P. Salkey +2
Gaussian processes are flexible function approximators, with inductive biases controlled by a covariance kernel. Learning the kernel is the key to representation learning and stron…