Search Machine Learning Repository:
All publications by Ryan P. Adams
authors venues years



A Gaussian Process Model of Quasar Spectral Energy Distributions
Andrew Miller, Albert Wu, Jeff Regier, Jon Mcauliffe, Dustin Lang, Mr. Prabhat, David Schlegel and Ryan P. Adams
Advances in Neural Information Processing Systems 28, 2015


Convolutional Networks on Graphs for Learning Molecular Fingerprints
David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-guzik and Ryan P. Adams
Advances in Neural Information Processing Systems 28, 2015


Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation
Scott Linderman, Matthew Johnson and Ryan P. Adams
Advances in Neural Information Processing Systems 28, 2015


Spectral Representations for Convolutional Neural Networks
Oren Rippel, Jasper Snoek and Ryan P. Adams
Advances in Neural Information Processing Systems 28, 2015


A framework for studying synaptic plasticity with neural spike train data
Scott Linderman, Christopher H. Stock and Ryan P. Adams
Advances in Neural Information Processing Systems 27, 2014


Multi-Task Bayesian Optimization
Kevin Swersky, Jasper Snoek and Ryan P. Adams
Advances in Neural Information Processing Systems 26, 2013


A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data
Jasper Snoek, Richard Zemel and Ryan P. Adams
Advances in Neural Information Processing Systems 26, 2013


Contrastive Learning Using Spectral Methods
James Y. Zou, Daniel Hsu, David C. Parkes and Ryan P. Adams
Advances in Neural Information Processing Systems 26, 2013


Message Passing Inference with Chemical Reaction Networks
Nils E. Napp and Ryan P. Adams
Advances in Neural Information Processing Systems 26, 2013


Cardinality Restricted Boltzmann Machines
Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard S. Zemel, Ruslan Salakhutdinov and Ryan P. Adams
Advances in Neural Information Processing Systems 25, 2012


Probabilistic n-Choose-k Models for Classification and Ranking
Kevin Swersky, Brendan J. Frey, Daniel Tarlow, Richard S. Zemel and Ryan P. Adams
Advances in Neural Information Processing Systems 25, 2012


Priors for Diversity in Generative Latent Variable Models
James T. Kwok and Ryan P. Adams
Advances in Neural Information Processing Systems 25, 2012


Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek, Hugo Larochelle and Ryan P. Adams
Advances in Neural Information Processing Systems 25, 2012


Training Restricted Boltzmann Machines on Word Observations
George Dahl, Hugo Larochelle and Ryan P. Adams
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Randomized Optimum Models for Structured Prediction
Daniel Tarlow, Ryan P. Adams and Richard S. Zemel
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012


On Nonparametric Guidance for Learning Autoencoder Representations
Jasper Snoek, Ryan P. Adams and Hugo Larochelle
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012


Slice sampling covariance hyperparameters of latent Gaussian models
Iain Murray and Ryan P. Adams
Advances in Neural Information Processing Systems 23, 2010


Tree-Structured Stick Breaking for Hierarchical Data
Zoubin Ghahramani, Michael I. Jordan and Ryan P. Adams
Advances in Neural Information Processing Systems 23, 2010


Elliptical slice sampling
Iain Murray, Ryan P. Adams and David Mackay
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Learning the Structure of Deep Sparse Graphical Models
Ryan P. Adams, Hanna M. Wallach and Zoubin Ghahramani
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities
Ryan P. Adams, Iain Murray and David Mackay
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Archipelago: nonparametric Bayesian semi-supervised learning
Ryan P. Adams and Zoubin Ghahramani
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Gaussian process product models for nonparametric nonstationarity
Ryan P. Adams and Oliver Stegle
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


The Gaussian Process Density Sampler
Iain Murray, David Mackay and Ryan P. Adams
Advances in Neural Information Processing Systems 21, 2008