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Found 8 papers, 7 papers with code
Stopping Bayesian Optimization with Probabilistic Regret Bounds
Bayesian optimization is a popular framework for efficiently finding high-quality solutions to difficult problems based on limited prior information.
A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions.
Pathwise Conditioning of Gaussian Processes
As Gaussian processes are used to answer increasingly complex questions, analytic solutions become scarcer and scarcer.
Efficiently Sampling Functions from Gaussian Process Posteriors
Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model's success hinges upon its ability to faithfully represent predictive uncertainty.
Maximizing acquisition functions for Bayesian optimization
Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process.
The reparameterization trick for acquisition functions
Bayesian optimization is a sample-efficient approach to solving global optimization problems.
Compressing Convolutional Neural Networks
Convolutional neural networks (CNN) are increasingly used in many areas of computer vision.
Compressing Neural Networks with the Hashing Trick
As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models.
