1 code implementation • 6 Feb 2024 • Andrew Stirn, David A. Knowles
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
no code implementations • 23 Aug 2023 • Peter Halmos, Jonathan Pillow, David A. Knowles
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.
no code implementations • 8 Aug 2023 • Daniel H. Um, David A. Knowles, Gail E. Kaiser
This paper demonstrates the utility of organized numerical representations of genes in research involving flat string gene formats (i. e., FASTA/FASTQ5).
1 code implementation • 18 Dec 2022 • Andrew Stirn, Hans-Hermann Wessels, Megan Schertzer, Laura Pereira, Neville E. Sanjana, David A. Knowles
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.
2 code implementations • 8 Jun 2020 • Andrew Stirn, David A. Knowles
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.
1 code implementation • NeurIPS 2019 • Andrew Stirn, Tony Jebara, David A. Knowles
We construct a new distribution for the simplex using the Kumaraswamy distribution and an ordered stick-breaking process.
1 code implementation • 4 Sep 2015 • David A. Knowles
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.
no code implementations • 26 Jun 2015 • Amar Shah, David A. Knowles, Zoubin Ghahramani
Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets.
no code implementations • 14 Aug 2014 • Creighton Heaukulani, David A. Knowles, Zoubin Ghahramani
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.
no code implementations • 17 Mar 2014 • Konstantina Palla, David A. Knowles, Zoubin Ghahramani
We present a nonparametric prior over reversible Markov chains.
no code implementations • 26 Sep 2013 • Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles, Zoubin Ghahramani
We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data.
no code implementations • 13 Mar 2013 • Konstantina Palla, David A. Knowles, Zoubin Ghahramani
The fundamental aim of clustering algorithms is to partition data points.
no code implementations • NeurIPS 2012 • Konstantina Palla, Zoubin Ghahramani, David A. Knowles
Factor analysis models effectively summarise the covariance structure of high dimensional data, but the solutions are typically hard to interpret.
2 code implementations • 28 Jun 2012 • Tim Salimans, David A. Knowles
We propose a general algorithm for approximating nonstandard Bayesian posterior distributions.
no code implementations • NeurIPS 2011 • David A. Knowles, Tom Minka
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.
1 code implementation • 19 Oct 2011 • Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani
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.
no code implementations • NeurIPS 2009 • Finale Doshi-Velez, Shakir Mohamed, Zoubin Ghahramani, David A. Knowles
Nonparametric Bayesian models provide a framework for flexible probabilistic modelling of complex datasets.