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Found 14 papers, 10 papers with code
Marginalizable Density Models
Probability density models based on deep networks have achieved remarkable success in modeling complex high-dimensional datasets.
Ranked #1 on
Density Estimation
on UCI POWER
Why Cold Posteriors? On the Suboptimal Generalization of Optimal Bayes Estimates
Recent works have shown that the predictive accuracy of Bayesian deep learning models exhibit substantial improvements when the posterior is raised to a 1/T power with T<1.
Amortized Probabilistic Detection of Communities in Graphs
While graph neural networks (GNNs) have been successful in encoding graph structures, existing GNN-based methods for community detection are limited by requiring knowledge of the number of communities in advance, in addition to lacking a proper probabilistic formulation to handle uncertainty.
Neural Permutation Processes
We introduce a neural architecture to perform amortized approximate Bayesian inference over latent random permutations of two sets of objects.
Spike Sorting using the Neural Clustering Process
We present a novel approach to spike sorting for high-density multielectrode probes using the Neural Clustering Process (NCP), a recently introduced neural architecture that performs scalable amortized approximate Bayesian inference for efficient probabilistic clustering.
Neural Clustering Processes
Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces.
Amortized Bayesian inference for clustering models
We develop methods for efficient amortized approximate Bayesian inference over posterior distributions of probabilistic clustering models, such as Dirichlet process mixture models.
Binary Bouncy Particle Sampler
The Bouncy Particle Sampler is a novel rejection-free non-reversible sampler for differentiable probability distributions over continuous variables.
Stochastic Bouncy Particle Sampler
We introduce a novel stochastic version of the non-reversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear.
Partition Functions from Rao-Blackwellized Tempered Sampling
Partition functions of probability distributions are important quantities for model evaluation and comparisons.
Taming the Noise in Reinforcement Learning via Soft Updates
We propose G-learning, a new off-policy learning algorithm that regularizes the value estimates by penalizing deterministic policies in the beginning of the learning process.
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
We develop an inference and optimal design procedure for recovering synaptic weights in neural microcircuits.
Bayesian spike inference from calcium imaging data
We present efficient Bayesian methods for extracting neuronal spiking information from calcium imaging data.
Neurons and Cognition Quantitative Methods Applications
Exact Hamiltonian Monte Carlo for Truncated Multivariate Gaussians
We present a Hamiltonian Monte Carlo algorithm to sample from multivariate Gaussian distributions in which the target space is constrained by linear and quadratic inequalities or products thereof.
Computation Applications
