Search Results for author:
Found 8 papers, 3 papers with code
Dataset Factory: A Toolchain For Generative Computer Vision Datasets
Generative AI workflows heavily rely on data-centric tasks - such as filtering samples by annotation fields, vector distances, or scores produced by custom classifiers.
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
It was based on tuning (validation set) performance of standard machine learning models on real datasets.
Scalable Global Optimization via Local Bayesian Optimization
This motivates the design of a local probabilistic approach for global optimization of large-scale high-dimensional problems.
Metropolis-Hastings Generative Adversarial Networks
We introduce the Metropolis-Hastings generative adversarial network (MH-GAN), which combines aspects of Markov chain Monte Carlo and GANs.
How well does your sampler really work?
We present a new data-driven benchmark system to evaluate the performance of new MCMC samplers.
Bayesian Hypernetworks
We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks.
A Model Explanation System: Latest Updates and Extensions
We propose a general model explanation system (MES) for "explaining" the output of black box classifiers.
Robust Filtering and Smoothing with Gaussian Processes
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models.
