Search Results for author:
Found 9 papers, 3 papers with code
Stochastic Gradient Descent for Gaussian Processes Done Right
We study the optimisation problem associated with Gaussian process regression using squared loss.
Minimal Random Code Learning with Mean-KL Parameterization
To achieve the desired compression rate, $D_{\mathrm{KL}}[Q_{\mathbf{w}} \Vert P_{\mathbf{w}}]$ must be constrained, which requires a computationally expensive annealing procedure under the conventional mean-variance (Mean-Var) parameterization for $Q_{\mathbf{w}}$.
Online Laplace Model Selection Revisited
The Laplace approximation provides a closed-form model selection objective for neural networks (NN).
Function-Space Regularization for Deep Bayesian Classification
Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior distributions to quantify uncertainty, increase safety and trust, and prevent overconfident and unpredictable behavior.
Beyond Intuition, a Framework for Applying GPs to Real-World Data
Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets.
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems.
Function-Space Variational Inference for Deep Bayesian Classification
Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior predictive distributions to quantify uncertainty, increase safety and trust, and prevent overconfident and unpredictable behavior.
Neural Linear Models with Functional Gaussian Process Priors
Neural linear models (NLM) and Gaussian processes (GP) are both examples of Bayesian linear regression on rich feature spaces.
Learning to Predict the 3D Layout of a Scene
We extend the network head by a 3D detection head, which predicts every degree of freedom of a 3D bounding box via classification.
