Meeds, Edward

12 publications

NeurIPS 2025 Gradient Multi-Normalization for Efficient LLM Training Meyer Scetbon, Chao Ma, Wenbo Gong, Edward Meeds

ICML 2025 SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds

MLHC 2023 AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires Melanie F. Pradier, Niranjani Prasad, Paidamoyo Chapfuwa, Sahra Ghalebikesabi, Maximilian Ilse, Steven Woodhouse, Rebecca Elyanow, Javier Zazo, Javier Gonzalez Hernandez, Julia Greissl, Edward Meeds

UAI 2022 Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin, Edward Meeds, Ricardo Henao

ICLR 2019 Deterministic Variational Inference for Robust Bayesian Neural Networks Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt

ICML 2019 Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems Geoffrey Roeder, Paul Grant, Andrew Phillips, Neil Dalchau, Edward Meeds

ICLR 2017 Soft Weight-Sharing for Neural Network Compression Karen Ullrich, Edward Meeds, Max Welling

UAI 2015 Hamiltonian ABC Edward Meeds, Robert Leenders, Max Welling

UAI 2014 GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation Edward Meeds, Max Welling

CVPR 2008 Learning Stick-Figure Models Using Nonparametric Bayesian Priors over Trees Edward Meeds, David A. Ross, Richard S. Zemel, Sam T. Roweis

NeurIPS 2006 Modeling Dyadic Data with Binary Latent Factors Edward Meeds, Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis

NeurIPS 2005 An Alternative Infinite Mixture of Gaussian Process Experts Edward Meeds, Simon Osindero