Search Results for author:
Found 17 papers, 7 papers with code
The VampPrior Mixture Model
Current clustering priors for deep latent variable models (DLVMs) require defining the number of clusters a-priori and are susceptible to poor initializations.
Ranked #2 on
Image Clustering
on Fashion-MNIST
System Identification for Continuous-time Linear Dynamical Systems
This paper addresses system identification for the continuous-discrete filter, with the aim of generalizing learning for the Kalman filter by relying on a solution to a continuous-time It\^o stochastic differential equation (SDE) for the latent state and covariance dynamics.
Vector Embeddings by Sequence Similarity and Context for Improved Compression, Similarity Search, Clustering, Organization, and Manipulation of cDNA Libraries
This paper demonstrates the utility of organized numerical representations of genes in research involving flat string gene formats (i. e., FASTA/FASTQ5).
Faithful Heteroscedastic Regression with Neural Networks
For a wide variety of network and task complexities, we find that mean estimates from existing heteroscedastic solutions can be significantly less accurate than those from an equivalently expressive mean-only model.
Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization
Brittle optimization has been observed to adversely impact model likelihoods for regression and VAEs when simultaneously fitting neural network mappings from a (random) variable onto the mean and variance of a dependent Gaussian variable.
A New Distribution on the Simplex with Auto-Encoding Applications
We construct a new distribution for the simplex using the Kumaraswamy distribution and an ordered stick-breaking process.
Stochastic gradient variational Bayes for gamma approximating distributions
While stochastic variational inference is relatively well known for scaling inference in Bayesian probabilistic models, related methods also offer ways to circumnavigate the approximation of analytically intractable expectations.
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process
Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets.
Beta diffusion trees and hierarchical feature allocations
We define the beta diffusion tree, a random tree structure with a set of leaves that defines a collection of overlapping subsets of objects, known as a feature allocation.
A reversible infinite HMM using normalised random measures
We present a nonparametric prior over reversible Markov chains.
The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models
We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data.
A dependent partition-valued process for multitask clustering and time evolving network modelling
The fundamental aim of clustering algorithms is to partition data points.
A nonparametric variable clustering model
Factor analysis models effectively summarise the covariance structure of high dimensional data, but the solutions are typically hard to interpret.
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
We propose a general algorithm for approximating nonstandard Bayesian posterior distributions.
Non-conjugate Variational Message Passing for Multinomial and Binary Regression
Variational Message Passing (VMP) is an algorithmic implementation of the Variational Bayes (VB) method which applies only in the special case of conjugate exponential family models.
Gaussian Process Regression Networks
We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes.
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process
Nonparametric Bayesian models provide a framework for flexible probabilistic modelling of complex datasets.
