Computational Perception Group

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Particular current focusses of the Computational Perception Group are: deep probabilistic learning, human-like learning, continual learning, k-shot learning, active learning, reinforcement learning, concept learning, Bayesian neural networks, Gaussian processes, spatio-temporal modelling, approximate inference, scalable and distributed inference, Monte Carlo methods, variational methods, expectation propagation, and Bayesian optimisation. We are also interested in the connection between machine learning and computation in the brain.

Please see the Publications section for an indication of relevant recent activity in these areas.

Previous and ongoing research includes the following:

Machine Perception

Statistical models for audio and video

Theoretical understanding of learning algorithms as probabilistic inference

Machine Vision

Learning invariances from natural images for object recognition

Statistical models for images

Machine Hearing

Synthesis of audio textures for computer games and artificial environments

Source separation

Neuroscience

Auditory processing as probabilistic inference

Neural implementations of approximate inference

Machine Learning

Approximate inference for time-series

Circular statistics and time-series

Signal Processing

Unifying signal processing and machine learning

Removing signal distortions using machine learning & signal processing

If you want to find out more about the core technical material that is relevant to the Group's reseach, please see the Group's recommended reading list.
 
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