Search Results for author: Matthew W. Hoffman

Found 12 papers, 6 papers with code

Modular Meta-Learning with Shrinkage

no code implementations NeurIPS 2020 Yutian Chen, Abram L. Friesen, Feryal Behbahani, Arnaud Doucet, David Budden, Matthew W. Hoffman, Nando de Freitas

Many real-world problems, including multi-speaker text-to-speech synthesis, can greatly benefit from the ability to meta-learn large models with only a few task-specific components.

Image Classification Meta-Learning +2

Large-Scale Visual Speech Recognition

no code implementations ICLR 2019 Brendan Shillingford, Yannis Assael, Matthew W. Hoffman, Thomas Paine, Cían Hughes, Utsav Prabhu, Hank Liao, Hasim Sak, Kanishka Rao, Lorrayne Bennett, Marie Mulville, Ben Coppin, Ben Laurie, Andrew Senior, Nando de Freitas

To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3, 886 hours of video).

Ranked #7 on Lipreading on LRS3-TED (using extra training data)

Lipreading speech-recognition +1

Learned Optimizers that Scale and Generalize

1 code implementation ICML 2017 Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein

Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks.

A General Framework for Constrained Bayesian Optimization using Information-based Search

1 code implementation30 Nov 2015 José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani

Of particular interest to us is to efficiently solve problems with decoupled constraints, in which subsets of the objective and constraint functions may be evaluated independently.

An Entropy Search Portfolio for Bayesian Optimization

no code implementations18 Jun 2014 Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman, Alexandre Bouchard-Côté, Nando de Freitas

How- ever, the performance of a Bayesian optimization method very much depends on its exploration strategy, i. e. the choice of acquisition function, and it is not clear a priori which choice will result in superior performance.

Exploiting correlation and budget constraints in Bayesian multi-armed bandit optimization

no code implementations27 Mar 2013 Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas

This problem is also known as fixed-budget best arm identification in the multi-armed bandit literature.

Cannot find the paper you are looking for? You can Submit a new open access paper.