Picture for Filip de Roos

Filip de Roos

A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization

Add code
Feb 22, 2021
Figure 1 for A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Figure 2 for A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Figure 3 for A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Figure 4 for A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Viaarxiv icon

High-Dimensional Gaussian Process Inference with Derivatives

Add code
Feb 15, 2021
Figure 1 for High-Dimensional Gaussian Process Inference with Derivatives
Figure 2 for High-Dimensional Gaussian Process Inference with Derivatives
Figure 3 for High-Dimensional Gaussian Process Inference with Derivatives
Figure 4 for High-Dimensional Gaussian Process Inference with Derivatives
Viaarxiv icon

Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization

Add code
Feb 20, 2019
Figure 1 for Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization
Figure 2 for Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization
Figure 3 for Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization
Figure 4 for Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization
Viaarxiv icon

Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning

Add code
Jun 01, 2017
Figure 1 for Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning
Figure 2 for Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning
Figure 3 for Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning
Figure 4 for Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning
Viaarxiv icon