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Found 7 papers, 5 papers with code
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
A broad class of stochastic volatility models are defined by systems of stochastic differential equations.
Bayesian Model Selection, the Marginal Likelihood, and Generalization
We provide a partial remedy through a conditional marginal likelihood, which we show is more aligned with generalization, and practically valuable for large-scale hyperparameter learning, such as in deep kernel learning.
Residual Pathway Priors for Soft Equivariance Constraints
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.
Rethinking Parameter Counting: Effective Dimensionality Revisited
Neural networks appear to have mysterious generalization properties when using parameter counting as a proxy for complexity.
Learning Invariances in Neural Networks from Training Data
Invariances to translations have imbued convolutional neural networks with powerful generalization properties.
Learning Invariances in Neural Networks
Invariances to translations have imbued convolutional neural networks with powerful generalization properties.
Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited
Neural networks appear to have mysterious generalization properties when using parameter counting as a proxy for complexity.
