Ted Meeds

Researcher

出版物

表示方法:

- Immunosequencing of the T-cell receptor repertoire reveals signatures specific for diagnosis and characterization of early Lyme disease
  
  Julia Greissl, Mitch Pesesky, Sudeb C. Dalai, Alison W. Rebman, Mark J. Soloski, Elizabeth J. Horn, Jennifer N. Dines, Rachel M. Gittelman, Thomas M. Snyder, Ryan O. Emerson, Ted Meeds, Thomas Manley, Ian M. Kaplan, Lance Baldo, Jonathan M. Carlson, Harlan S. Robins, John N. Aucott
  
  medRxiv | August 2021
  
  DOI PDF Publication プロジェクト
- Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
  
  Geoffrey Roeder, Paul Grant, Andrew Phillips, Neil Dalchau, Ted Meeds
  
  ICML 2019 | June 2019
  
  Author's Version PDF プロジェクトプロジェクトプロジェクト
- Deterministic Variational Inference for Robust Bayesian Neural Networks
  
  Anqi Wu, Sebastian Nowozin, Ted Meeds (edmeeds), Richard E. Turner, Jose Miguel Hernadez-Lobato, Alexander L. Gaunt
  
  International Conference on Learning Representations | May 2019
  
  Publication
- Soft Weight-Sharing for Neural Network Compression
  
  Karen Ullrich, Max Welling, Ted Meeds
  
  International Conference on Representation Learning (ICLR) | July 2017
  
  公開日
- Automatic Variational ABC
  
  Alexander Moreno, Tameem Adel, Ted Meeds, James M. Rehg, Max Welling
  
  June 2016
  
  公開日公開日
- Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
  
  Ted Meeds, Max Welling
  
  Neural Information Processing Systems (NIPS) | December 2015
  
  公開日
- POPE: post optimization posterior evaluation of likelihood free models
  
  Ted Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John Lowengrub, Max Welling
  
  BMC Bioinformatics | August 2015, 巻26
  
  公開日公開日公開日
- MLitB: machine learning in the browser
  
  Ted Meeds, Remco Hendriks, Said Al Farady, Magiel Bruntink, Max Welling
  
  PeerJ Computer Science | July 2015, 巻e11
  
  公開日公開日
- Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation
  
  Michael Chiang, Amanda Cinquin, Adrian Paz, Ted Meeds (edmeeds), Christopher A. Price, Max Welling, Olivier Cinquin
  
  BMC Biology | July 2015, 巻13
  
  公開日公開日
- Hamiltonian ABC
  
  Ted Meeds, Robert Leenders, Max Welling
  
  Uncertainty in Artificial Intelligence (UAI) | July 2015
  
  公開日
- GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
  
  Ted Meeds, Max Welling
  
  Uncertainty in Artificial Intelligence | July 2014
  
  公開日
- Learning stick-figure models using nonparametric Bayesian priors over trees
  
  Ted Meeds, David A. Ross, Richard Zemel, Sam Roweis
  
  Conference on Computer Vision and Pattern Recognition (CVPR) | August 2008
  
  公開日
- Nonparametric Bayesian Biclustering
  
  Ted Meeds, Sam Roweis
  
  UTML TR 2007–001 | January 2007
  
  公開日
- Modeling Dyadic Data with Binary Latent Factors
  
  Ted Meeds, Zoubin Ghahramani, Radford Neal, Sam Roweis
  
  Neural Information Processing Systems | December 2006
  
  公開日
- An Alternative Infinite Mixture Of Gaussian Process Experts
  
  Ted Meeds, Simon Osindero
  
  Neural Information Processing Systems | December 2005
  
  公開日
- Novelty Detection Model Selection Using Volume Estimation
  
  Ted Meeds
  
  UTML TR 2005–004 | August 2005
  
  公開日

- Immunosequencing of the T-cell receptor repertoire reveals signatures specific for diagnosis and characterization of early Lyme disease
  
  Julia Greissl, Mitch Pesesky, Sudeb C. Dalai, Alison W. Rebman, Mark J. Soloski, Elizabeth J. Horn, Jennifer N. Dines, Rachel M. Gittelman, Thomas M. Snyder, Ryan O. Emerson, Ted Meeds, Thomas Manley, Ian M. Kaplan, Lance Baldo, Jonathan M. Carlson, Harlan S. Robins, John N. Aucott
  
  medRxiv | August 2021
  
  DOI PDF Publication プロジェクト
- Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation
  
  Michael Chiang, Amanda Cinquin, Adrian Paz, Ted Meeds (edmeeds), Christopher A. Price, Max Welling, Olivier Cinquin
  
  BMC Biology | July 2015, 巻13
  
  公開日公開日
- Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
  
  Geoffrey Roeder, Paul Grant, Andrew Phillips, Neil Dalchau, Ted Meeds
  
  ICML 2019 | June 2019
  
  Author's Version PDF プロジェクトプロジェクトプロジェクト
- Deterministic Variational Inference for Robust Bayesian Neural Networks
  
  Anqi Wu, Sebastian Nowozin, Ted Meeds (edmeeds), Richard E. Turner, Jose Miguel Hernadez-Lobato, Alexander L. Gaunt
  
  International Conference on Learning Representations | May 2019
  
  Publication
- Soft Weight-Sharing for Neural Network Compression
  
  Karen Ullrich, Max Welling, Ted Meeds
  
  International Conference on Representation Learning (ICLR) | July 2017
  
  公開日
- Automatic Variational ABC
  
  Alexander Moreno, Tameem Adel, Ted Meeds, James M. Rehg, Max Welling
  
  June 2016
  
  公開日公開日
- Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
  
  Ted Meeds, Max Welling
  
  Neural Information Processing Systems (NIPS) | December 2015
  
  公開日
- POPE: post optimization posterior evaluation of likelihood free models
  
  Ted Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John Lowengrub, Max Welling
  
  BMC Bioinformatics | August 2015, 巻26
  
  公開日公開日公開日
- MLitB: machine learning in the browser
  
  Ted Meeds, Remco Hendriks, Said Al Farady, Magiel Bruntink, Max Welling
  
  PeerJ Computer Science | July 2015, 巻e11
  
  公開日公開日
- Hamiltonian ABC
  
  Ted Meeds, Robert Leenders, Max Welling
  
  Uncertainty in Artificial Intelligence (UAI) | July 2015
  
  公開日
- GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
  
  Ted Meeds, Max Welling
  
  Uncertainty in Artificial Intelligence | July 2014
  
  公開日
- Nonparametric Bayesian Biclustering
  
  Ted Meeds, Sam Roweis
  
  UTML TR 2007–001 | January 2007
  
  公開日
- Modeling Dyadic Data with Binary Latent Factors
  
  Ted Meeds, Zoubin Ghahramani, Radford Neal, Sam Roweis
  
  Neural Information Processing Systems | December 2006
  
  公開日
- An Alternative Infinite Mixture Of Gaussian Process Experts
  
  Ted Meeds, Simon Osindero
  
  Neural Information Processing Systems | December 2005
  
  公開日
- Novelty Detection Model Selection Using Volume Estimation
  
  Ted Meeds
  
  UTML TR 2005–004 | August 2005
  
  公開日
- MLitB: machine learning in the browser
  
  Ted Meeds, Remco Hendriks, Said Al Farady, Magiel Bruntink, Max Welling
  
  PeerJ Computer Science | July 2015, 巻e11
  
  公開日公開日
- Learning stick-figure models using nonparametric Bayesian priors over trees
  
  Ted Meeds, David A. Ross, Richard Zemel, Sam Roweis
  
  Conference on Computer Vision and Pattern Recognition (CVPR) | August 2008
  
  公開日

- Immunosequencing of the T-cell receptor repertoire reveals signatures specific for diagnosis and characterization of early Lyme disease
  
  Julia Greissl, Mitch Pesesky, Sudeb C. Dalai, Alison W. Rebman, Mark J. Soloski, Elizabeth J. Horn, Jennifer N. Dines, Rachel M. Gittelman, Thomas M. Snyder, Ryan O. Emerson, Ted Meeds, Thomas Manley, Ian M. Kaplan, Lance Baldo, Jonathan M. Carlson, Harlan S. Robins, John N. Aucott
  
  medRxiv | August 2021
  
  DOI PDF Publication プロジェクト
- POPE: post optimization posterior evaluation of likelihood free models
  
  Ted Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John Lowengrub, Max Welling
  
  BMC Bioinformatics | August 2015, 巻26
  
  公開日公開日公開日
- MLitB: machine learning in the browser
  
  Ted Meeds, Remco Hendriks, Said Al Farady, Magiel Bruntink, Max Welling
  
  PeerJ Computer Science | July 2015, 巻e11
  
  公開日公開日
- Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation
  
  Michael Chiang, Amanda Cinquin, Adrian Paz, Ted Meeds (edmeeds), Christopher A. Price, Max Welling, Olivier Cinquin
  
  BMC Biology | July 2015, 巻13
  
  公開日公開日
- Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
  
  Geoffrey Roeder, Paul Grant, Andrew Phillips, Neil Dalchau, Ted Meeds
  
  ICML 2019 | June 2019
  
  Author's Version PDF プロジェクトプロジェクトプロジェクト
- Deterministic Variational Inference for Robust Bayesian Neural Networks
  
  Anqi Wu, Sebastian Nowozin, Ted Meeds (edmeeds), Richard E. Turner, Jose Miguel Hernadez-Lobato, Alexander L. Gaunt
  
  International Conference on Learning Representations | May 2019
  
  Publication
- Soft Weight-Sharing for Neural Network Compression
  
  Karen Ullrich, Max Welling, Ted Meeds
  
  International Conference on Representation Learning (ICLR) | July 2017
  
  公開日
- Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
  
  Ted Meeds, Max Welling
  
  Neural Information Processing Systems (NIPS) | December 2015
  
  公開日
- Hamiltonian ABC
  
  Ted Meeds, Robert Leenders, Max Welling
  
  Uncertainty in Artificial Intelligence (UAI) | July 2015
  
  公開日
- GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
  
  Ted Meeds, Max Welling
  
  Uncertainty in Artificial Intelligence | July 2014
  
  公開日
- Learning stick-figure models using nonparametric Bayesian priors over trees
  
  Ted Meeds, David A. Ross, Richard Zemel, Sam Roweis
  
  Conference on Computer Vision and Pattern Recognition (CVPR) | August 2008
  
  公開日
- Modeling Dyadic Data with Binary Latent Factors
  
  Ted Meeds, Zoubin Ghahramani, Radford Neal, Sam Roweis
  
  Neural Information Processing Systems | December 2006
  
  公開日
- An Alternative Infinite Mixture Of Gaussian Process Experts
  
  Ted Meeds, Simon Osindero
  
  Neural Information Processing Systems | December 2005
  
  公開日
- Automatic Variational ABC
  
  Alexander Moreno, Tameem Adel, Ted Meeds, James M. Rehg, Max Welling
  
  June 2016
  
  公開日公開日
- Nonparametric Bayesian Biclustering
  
  Ted Meeds, Sam Roweis
  
  UTML TR 2007–001 | January 2007
  
  公開日
- Novelty Detection Model Selection Using Volume Estimation
  
  Ted Meeds
  
  UTML TR 2005–004 | August 2005
  
  公開日

Ted Meeds

出版物

Immunosequencing of the T-cell receptor repertoire reveals signatures specific for diagnosis and characterization of early Lyme disease

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

Deterministic Variational Inference for Robust Bayesian Neural Networks

Soft Weight-Sharing for Neural Network Compression

Automatic Variational ABC

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference

POPE: post optimization posterior evaluation of likelihood free models

MLitB: machine learning in the browser

Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation

Hamiltonian ABC

GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation

Learning stick-figure models using nonparametric Bayesian priors over trees

Nonparametric Bayesian Biclustering

Modeling Dyadic Data with Binary Latent Factors

An Alternative Infinite Mixture Of Gaussian Process Experts

Novelty Detection Model Selection Using Volume Estimation

Immunosequencing of the T-cell receptor repertoire reveals signatures specific for diagnosis and characterization of early Lyme disease

Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

Deterministic Variational Inference for Robust Bayesian Neural Networks

Soft Weight-Sharing for Neural Network Compression

Automatic Variational ABC

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference

POPE: post optimization posterior evaluation of likelihood free models

MLitB: machine learning in the browser

Hamiltonian ABC

GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation

Nonparametric Bayesian Biclustering

Modeling Dyadic Data with Binary Latent Factors

An Alternative Infinite Mixture Of Gaussian Process Experts

Novelty Detection Model Selection Using Volume Estimation

MLitB: machine learning in the browser

Learning stick-figure models using nonparametric Bayesian priors over trees

Immunosequencing of the T-cell receptor repertoire reveals signatures specific for diagnosis and characterization of early Lyme disease

POPE: post optimization posterior evaluation of likelihood free models

MLitB: machine learning in the browser

Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

Deterministic Variational Inference for Robust Bayesian Neural Networks

Soft Weight-Sharing for Neural Network Compression

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference

Hamiltonian ABC

GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation

Learning stick-figure models using nonparametric Bayesian priors over trees

Modeling Dyadic Data with Binary Latent Factors

An Alternative Infinite Mixture Of Gaussian Process Experts

Automatic Variational ABC

Nonparametric Bayesian Biclustering

Novelty Detection Model Selection Using Volume Estimation