2.1k citations · 9.2k across the 77 of their papers we have counts for
6 papers · 1 filter
Deep Fried Convnets
Zichao Yang, Marcin Moczulski, Misha Denil +4
The fully connected layers of a deep convolutional neural network typically contain over 90% of the network parameters, and consume the majority of the memory required to store the…
Heteroscedastic Treed Bayesian Optimisation
John-Alexander M. Assael, Ziyu Wang, Bobak Shahriari +1
Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-…
Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters
Ziyu Wang, Nando de Freitas
Bayesian optimisation has gained great popularity as a tool for optimising the parameters of machine learning algorithms and models. Somewhat ironically, setting up the hyper-param…
An Entropy Search Portfolio for Bayesian Optimization
Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman +2
Bayesian optimization is a sample-efficient method for black-box global optimization. How- ever, the performance of a Bayesian optimization method very much depends on its explorat…
A Deep Architecture for Semantic Parsing
Edward Grefenstette, Phil Blunsom, Nando de Freitas +1
Many successful approaches to semantic parsing build on top of the syntactic analysis of text, and make use of distributional representations or statistical models to match parses…
Bayesian Multi-Scale Optimistic Optimization
Ziyu Wang, Babak Shakibi, Lin Jin +1
Bayesian optimization is a powerful global optimization technique for expensive black-box functions. One of its shortcomings is that it requires auxiliary optimization of an acquis…