1 citations · 1 across the 5 of their papers we have counts for
5 papers
Adaptive Cluster Expansion (ACE): A Hierarchical Bayesian Network
Stephen Luttrell
Using the maximum entropy method, we derive the "adaptive cluster expansion" (ACE), which can be trained to estimate probability density functions in high dimensional spaces. The m…
Self-Organised Factorial Encoding of a Toroidal Manifold
Stephen Luttrell
It is shown analytically how a neural network can be used optimally to encode input data that is derived from a toroidal manifold. The case of a 2-layer network is considered, wher…
Invariant Stochastic Encoders
Stephen Luttrell
The theory of stochastic vector quantisers (SVQ) has been extended to allow the quantiser to develop invariances, so that only "large" degrees of freedom in the input vector are re…
Using Stochastic Encoders to Discover Structure in Data
Stephen Luttrell
In this paper a stochastic generalisation of the standard Linde-Buzo-Gray (LBG) approach to vector quantiser (VQ) design is presented, in which the encoder is implemented as the sa…
Using Self-Organising Mappings to Learn the Structure of Data Manifolds
Stephen Luttrell
In this paper it is shown how to map a data manifold into a simpler form by progressively discarding small degrees of freedom. This is the key to self-organising data fusion, where…