Computational Perception Group

home | members | publications | directions | research | presentations | teaching | vacancies | personal


The Computational Perception Group is run by Richard Turner. Its research spans the following areas:

  1. machine learning which provides a theoretical framework for learning and making inferences from data in order to make decisions.
  2. computer perception and cognition which builds automatic systems for processing and understanding images, sounds and videos
  3. statistical signal processing which uses techniques from statistics and machine learning to develop new signal processing methods
  4. machine learning for climate science which employs advanced machine learning methods to improve predictions for climate modelling and forecasting to facilitate decision making

Particular current focusses of the group are: deep probabilistic learning, human-like learning, continual learning, k-shot learning, active learning, transfer learning, reinforcement learning, concept learning, probabilistic models, Bayesian statistics, 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.



opening image


Selected News

18 / 10 / 19 Richard Turner awarded an EPSRC Prosperity Partnership
03 / 09 / 19 3 papers accepted to NeurIPS2019: Practical Deep Learning with Bayesian Principles, Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model and Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes (spotlight oral)
02 / 09 / 19 A new draft paper on Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks
01 / 09 / 19 Dr. Turner becomes Director of the Machine Learning and Machine Intelligence MPhil.
01 / 03 / 19 The new UKRI Centre for Doctoral Training in AI for the study of Environmental Risks (AI4ER) is announced. Dr. Turner is a co-Director.
23 / 12 / 18 2 papers accepted to AISTATS2019: The Gaussian Process Autoregressive Regression Model (GPAR) and Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning (oral)
22 / 12 / 18 2 papers accepted to ICLR2019: Meta-Learning Probabilistic Inference for Prediction and Deterministic Variational Inference for Robust Bayesian Neural Networks (oral)
01 / 10 / 18 Richard Turner promoted to University Reader (equiv. Associate Prof. USA)
05 / 09 / 18 2 papers accepted to NeurIPS2018: Infinite-Horizon Gaussian Processes and Geometrically Coupled Monte Carlo Sampling (spotlight oral)
04 / 09 / 18 Tutorial on GPs presented at the Machine Learning Summer School in Madrid: Introduction to GPs, GP models, connections and applications, and Scaling GPs and handling non-linear models.
02 / 07 / 18 Tutorial on Bayesian Deep Learning at the LVA ICA meeting
11 / 05 / 18 2 papers accepted to ICML2018: Structured Evolution with Compact Architectures for Scalable Policy Optimization and The Mirage of Action-Dependent Baselines in Reinforcement Learning
29 / 01 / 18 3 papers accepted to ICLR2018: Variational Continual Learning, Gaussian Process Behaviour in Wide Deep Neural Networks and Gradient Estimators for Implicit Models
01 / 01 / 18 The Geometry of Random Features accepted to AISTATS2018
23 / 10 / 17 A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation published in JMLR
05 / 09 / 17 Streaming Sparse Gaussian Process Approximations accepted to NIPS2017
05 / 09 / 17 Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning accepted to NIPS2017
02 / 06 / 17 A new draft paper: Discriminative k-shot learning using probabilistic models
15 / 05 / 17 Two papers [1, 2] accepted to ICML2017
06 / 02 / 17 Q-Prop: Sample-Efficient Policy Gradient with an Off-Policy Critic accepted for oral presentation at ICLR
23 / 11 / 16 Gaussian Processes: From the Basics to the State-of-the-Art, Imperial's ML tutorial series [ slides | video ]
16 / 09 / 16 Machine Learning for Signal Processing keynote presentation [ slides | video ]
19 / 05 / 15 Richard Turner awarded a Cambridge Students' Union Teaching Award for Lecturing
more...
 
This site is powered by FoswikiCopyright © by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding Foswiki? Send feedback.    Privacy policy