78 citations · 272 across the 17 of their papers we have counts for
6 papers · 1 filter
Dimensionality Reduction as Probabilistic Inference
Aditya Ravuri, Francisco Vargas, Vidhi Lalchand +1
Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data. DR is a critical st…
Manifold Alignment Determination: finding correspondences across different data views
Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek
We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities. The approach is capable of learning c…
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology
Mu Niu, Zhenwen Dai, Neil Lawrence +1
Quantitative modeling of post-transcriptional regulation process is a challenging problem in systems biology. A mechanical model of the regulatory process needs to be able to descr…
Nested Variational Compression in Deep Gaussian Processes
James Hensman, Neil D. Lawrence
Deep Gaussian processes provide a flexible approach to probabilistic modelling of data using either supervised or unsupervised learning. For tractable inference approximations to t…
Metrics for Probabilistic Geometries
Alessandra Tosi, Søren Hauberg, Alfredo Vellido +1
We investigate the geometrical structure of probabilistic generative dimensionality reduction models using the tools of Riemannian geometry. We explicitly define a distribution ove…
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied. However, the current approach…