528 citations · 4.4k across the 100 of their papers we have counts for
5 papers · 1 filter
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning
Andrew Gordon Wilson, Jason Yosinski, Patrice Simard +2
This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning, held in Long Beach, California, USA on December 7, 2017
Scalable Log Determinants for Gaussian Process Kernel Learning
Kun Dong, David Eriksson, Hannes Nickisch +2
For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compu…
Bayesian GAN
Yunus Saatchi, Andrew Gordon Wilson
Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. We present a practi…
Multimodal Word Distributions
Ben Athiwaratkun, Andrew Gordon Wilson
Word embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multipl…
Bayesian Optimization with Gradients
Jian Wu, Matthias Poloczek, Andrew Gordon Wilson +1
Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimi…