NIPS Proceedings
^{β}
Books
Ryan P. Adams
20 Papers
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
(2017)
Reducing Reparameterization Gradient Variance
(2017)
Bayesian latent structure discovery from multi-neuron recordings
(2016)
Composing graphical models with neural networks for structured representations and fast inference
(2016)
A Gaussian Process Model of Quasar Spectral Energy Distributions
(2015)
Convolutional Networks on Graphs for Learning Molecular Fingerprints
(2015)
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation
(2015)
Spectral Representations for Convolutional Neural Networks
(2015)
A framework for studying synaptic plasticity with neural spike train data
(2014)
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data
(2013)
Contrastive Learning Using Spectral Methods
(2013)
Message Passing Inference with Chemical Reaction Networks
(2013)
Multi-Task Bayesian Optimization
(2013)
Cardinality Restricted Boltzmann Machines
(2012)
Practical Bayesian Optimization of Machine Learning Algorithms
(2012)
Priors for Diversity in Generative Latent Variable Models
(2012)
Probabilistic n-Choose-k Models for Classification and Ranking
(2012)
Slice sampling covariance hyperparameters of latent Gaussian models
(2010)
Tree-Structured Stick Breaking for Hierarchical Data
(2010)
The Gaussian Process Density Sampler
(2008)