Computational Perception Group
home | members | publications | directions | research | presentations | teaching | vacancies | personal 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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