no code implementations • 30 Mar 2024 • Anna Vaughan, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, J. Scott Hosking, Richard E. Turner

Machine learning is revolutionising medium-range weather prediction.

no code implementations • 4 Mar 2024 • James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric Nalisnick, José Miguel Hernández-Lobato

Correctly capturing the symmetry transformations of data can lead to efficient models with strong generalization capabilities, though methods incorporating symmetries often require prior knowledge.

no code implementations • 6 Feb 2024 • Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou, Aliaksandra Shysheya, Andrew Y. K. Foong, Bruno Mlodozeniec

Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including image and video generation, protein and material synthesis, weather forecasting, and neural surrogates of partial differential equations.

no code implementations • 5 Feb 2024 • Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani

Adaptive gradient optimizers like Adam(W) are the default training algorithms for many deep learning architectures, such as transformers.

no code implementations • 3 Jan 2024 • Massimiliano Patacchiola, Aliaksandra Shysheya, Katja Hofmann, Richard E. Turner

In this paper, we propose a novel solution to these challenges by exploiting transformers to define a new class of neural flows called Transformer Neural Autoregressive Flows (T-NAFs).

1 code implementation • 9 Dec 2023 • Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani

Second-order methods for deep learning -- such as KFAC -- can be useful for neural net training.

no code implementations • 28 Nov 2023 • Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen

In this paper, we introduce a new nonlinear ICA framework that employs $t$-process (TP) latent components which apply naturally to data with higher-dimensional dependency structures, such as spatial and spatio-temporal data.

no code implementations • 16 Nov 2023 • Lorenzo Bonito, James Requeima, Aliaksandra Shysheya, Richard E. Turner

Over the last few years, Neural Processes have become a useful modelling tool in many application areas, such as healthcare and climate sciences, in which data are scarce and prediction uncertainty estimates are indispensable.

no code implementations • NeurIPS 2023 • Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig

In this work, we identify two different settings of linear weight-sharing layers which motivate two flavours of K-FAC -- $\textit{expand}$ and $\textit{reduce}$.

1 code implementation • 30 Oct 2023 • Jonas Scholz, Tom R. Andersson, Anna Vaughan, James Requeima, Richard E. Turner

On held-out weather stations, Sim2Real training substantially outperforms the same model architecture trained only with reanalysis data or only with station data, showing that reanalysis data can serve as a stepping stone for learning from real observations.

1 code implementation • 18 Oct 2023 • Jonathan So, Richard E. Turner

In this work we propose a novel technique for tackling such issues, which involves reframing the optimisation as one with respect to the parameters of a surrogate distribution, for which computing the natural gradient is easy.

1 code implementation • 6 Jul 2023 • Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex Gardner, ST John, Hong Ge, Richard E. Turner

Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets.

1 code implementation • 23 Jun 2023 • Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann, Richard E. Turner

Despite its simplicity this baseline is competitive with meta-learning methods on a variety of conditions and is able to imitate target policies trained on unseen variations of the original environment.

Few-Shot Image Classification
Few-Shot Imitation Learning
**+3**

no code implementations • 20 Apr 2023 • Richard E. Turner

The transformer is a neural network component that can be used to learn useful representations of sequences or sets of data-points.

1 code implementation • 25 Mar 2023 • Wessel P. Bruinsma, Stratis Markou, James Requiema, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner

Our work provides an example of how ideas from neural distribution estimation can benefit neural processes, and motivates research into the AR deployment of other neural process models.

no code implementations • ICCV 2023 • Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, Richard E. Turner

In this work, we develop a baseline method, First Session Adaptation (FSA), that sheds light on the efficacy of existing CIL approaches and allows us to assess the relative performance contributions from head and body adaption.

no code implementations • 23 Nov 2022 • Elre T. Oldewage, John Bronskill, Richard E. Turner

This paper examines the robustness of deployed few-shot meta-learning systems when they are fed an imperceptibly perturbed few-shot dataset.

1 code implementation • 18 Nov 2022 • Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou, James Requeima, Alejandro Coca-Castro, Anna Vaughan, Anna-Louise Ellis, Matthew A. Lazzara, Dani Jones, J. Scott Hosking, Richard E. Turner

This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.

1 code implementation • 29 Oct 2022 • Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser

Ice cores record crucial information about past climate.

1 code implementation • 23 Sep 2022 • Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela

In this paper, we present differentially private partitioned variational inference, the first general framework for learning a variational approximation to a Bayesian posterior distribution in the federated learning setting while minimising the number of communication rounds and providing differential privacy guarantees for data subjects.

1 code implementation • 11 Sep 2022 • Vidhi Lalchand, Kenza Tazi, Talay M. Cheema, Richard E. Turner, Scott Hosking

We account for the spatial variation in precipitation with a non-stationary Gibbs kernel parameterised with an input dependent lengthscale.

1 code implementation • 1 Sep 2022 • Saurav Jha, Dong Gong, Xuesong Wang, Richard E. Turner, Lina Yao

We shed light on their potential to bring several recent advances in other deep learning domains under one umbrella.

1 code implementation • 20 Jun 2022 • Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E. Turner

In this paper we push this Pareto frontier in the few-shot image classification setting with a key contribution: a new adaptive block called Contextual Squeeze-and-Excitation (CaSE) that adjusts a pretrained neural network on a new task to significantly improve performance with a single forward pass of the user data (context).

Ranked #3 on Few-Shot Image Classification on Meta-Dataset

no code implementations • 18 May 2022 • Isabel Chien, Nina Deliu, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus

While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice.

no code implementations • 16 Mar 2022 • Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner

Existing approaches which model output dependencies, such as Neural Processes (NPs; Garnelo et al., 2018b) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to train or prohibitively expensive.

1 code implementation • 14 Mar 2022 • Wessel P. Bruinsma, Martin Tegnér, Richard E. Turner

The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a model for signals with complex spectral structure.

1 code implementation • 24 Feb 2022 • Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Stratis Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner

Variational inference (VI) has become the method of choice for fitting many modern probabilistic models.

2 code implementations • NeurIPS 2021 • John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard E. Turner

This limitation arises because a task's entire support set, which can contain up to 1000 images, must be processed before an optimization step can be taken.

1 code implementation • 24 Jun 2021 • Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin, Richard E. Turner

This work identifies the crucial link between the two problems and investigates the Novelty Detection problem under the Continual Learning setting.

1 code implementation • pproximateinference AABI Symposium 2021 • Will Tebbutt, Arno Solin, Richard E. Turner

Pseudo-point approximations, one of the gold-standard methods for scaling GPs to large data sets, are well suited for handling off-the-grid spatial data.

1 code implementation • NeurIPS 2021 • Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner

Interestingly, this lower bound recovers the Chernoff test set bound if the posterior is equal to the prior.

no code implementations • 12 Apr 2021 • Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang

While deep learning has obtained state-of-the-art results in many applications, the adaptation of neural network architectures to incorporate new output features remains a challenge, as neural networks are commonly trained to produce a fixed output dimension.

1 code implementation • NeurIPS Workshop ICBINB 2020 • Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison

Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference.

1 code implementation • 20 Jan 2021 • Anna Vaughan, Will Tebbutt, J. Scott Hosking, Richard E. Turner

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs).

1 code implementation • pproximateinference AABI Symposium 2021 • Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong, Jonathan Gordon, Richard E. Turner

Neural Processes (NPs; Garnelo et al., 2018a, b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes.

no code implementations • ICLR 2021 • Noel Loo, Siddharth Swaroop, Richard E. Turner

One strand of research has used probabilistic regularization for continual learning, with two of the main approaches in this vein being Online Elastic Weight Consolidation (Online EWC) and Variational Continual Learning (VCL).

1 code implementation • 20 Oct 2020 • Matthew Ashman, Jonathan So, Will Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner

Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering.

no code implementations • 24 Jul 2020 • Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman

In this paper we provide a firm theoretical interpretation for infinite spatial generation, by drawing connections to spatial stochastic processes.

no code implementations • 23 Jul 2020 • Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang

In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers.

2 code implementations • NeurIPS 2020 • Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner

Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data.

1 code implementation • NeurIPS 2020 • Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan

Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past.

2 code implementations • ICML 2020 • John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner

Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines.

1 code implementation • NeurIPS 2019 • Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang

In this paper, we address the ice-start problem, i. e., the challenge of deploying machine learning models when only a little or no training data is initially available, and acquiring each feature element of data is associated with costs.

no code implementations • 26 Nov 2019 • Bo-Hsiang Tseng, Marek Rei, Paweł Budzianowski, Richard E. Turner, Bill Byrne, Anna Korhonen

Dialogue systems benefit greatly from optimizing on detailed annotations, such as transcribed utterances, internal dialogue state representations and dialogue act labels.

1 code implementation • 24 Nov 2019 • Mrinank Sharma, Michael Hutchinson, Siddharth Swaroop, Antti Honkela, Richard E. Turner

This setting is known as federated learning, in which privacy is a key concern.

no code implementations • ICLR 2020 • Tameem Adel, Han Zhao, Richard E. Turner

Approaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner.

1 code implementation • ICML 2020 • Wessel P. Bruinsma, Eric Perim, Will Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner

Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-temporal modelling.

3 code implementations • ICLR 2020 • Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner

We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data.

no code implementations • 5 Sep 2019 • Jan Stühmer, Richard E. Turner, Sebastian Nowozin

Second, we demonstrate that the proposed prior encourages a disentangled latent representation which facilitates learning of disentangled representations.

2 code implementations • NeurIPS 2020 • Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner

While Bayesian neural networks (BNNs) hold the promise of being flexible, well-calibrated statistical models, inference often requires approximations whose consequences are poorly understood.

no code implementations • 27 Jun 2019 • Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner

We describe a limitation in the expressiveness of the predictive uncertainty estimate given by mean-field variational inference (MFVI), a popular approximate inference method for Bayesian neural networks.

1 code implementation • NeurIPS 2019 • James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner

We introduce a conditional neural process based approach to the multi-task classification setting for this purpose, and establish connections to the meta-learning and few-shot learning literature.

Ranked #6 on Few-Shot Image Classification on Meta-Dataset Rank

1 code implementation • NeurIPS 2019 • Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan

Importantly, the benefits of Bayesian principles are preserved: predictive probabilities are well-calibrated, uncertainties on out-of-distribution data are improved, and continual-learning performance is boosted.

no code implementations • 24 May 2019 • Josef Schlittenlacher, Richard E. Turner, Brian C. J. Moore

The DNN was trained using the output of a more complex model, called the Cambridge loudness model.

1 code implementation • 6 May 2019 • Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. Turner

In the continual learning setting, tasks are encountered sequentially.

no code implementations • ICLR 2019 • Tameem Adel, Cuong V. Nguyen, Richard E. Turner, Zoubin Ghahramani, Adrian Weller

We present a framework for interpretable continual learning (ICL).

no code implementations • NeurIPS 2018 • Mark Rowland, Krzysztof M. Choromanski, François Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E. Turner, Adrian Weller

Monte Carlo sampling in high-dimensional, low-sample settings is important in many machine learning tasks.

no code implementations • 27 Nov 2018 • Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop, Richard E. Turner

Second, the granularity of the updates e. g. whether the updates are local to each data point and employ message passing or global.

1 code implementation • NeurIPS 2018 • Arno Solin, James Hensman, Richard E. Turner

The complexity is still cubic in the state dimension $m$ which is an impediment to practical application.

3 code implementations • ICLR 2019 • Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt

We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances.

1 code implementation • ICLR 2019 • Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner

2) We introduce VERSA, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass.

1 code implementation • 22 May 2018 • Aapo Hyvarinen, Hiroaki Sasaki, Richard E. Turner

Here, we propose a general framework for nonlinear ICA, which, as a special case, can make use of temporal structure.

2 code implementations • ICLR 2018 • Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani

Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties.

no code implementations • ICML 2018 • Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller

We present a new method of blackbox optimization via gradient approximation with the use of structured random orthogonal matrices, providing more accurate estimators than baselines and with provable theoretical guarantees.

1 code implementation • ICML 2018 • George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine

Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance.

no code implementations • 22 Feb 2018 • Wessel Bruinsma, Richard E. Turner

We present the Causal Gaussian Process Convolution Model (CGPCM), a doubly nonparametric model for causal, spectrally complex dynamical phenomena.

1 code implementation • 20 Feb 2018 • James Requeima, Will Tebbutt, Wessel Bruinsma, Richard E. Turner

Multi-output regression models must exploit dependencies between outputs to maximise predictive performance.

no code implementations • 14 Feb 2018 • Brian L. Trippe, Richard E. Turner

Modeling complex conditional distributions is critical in a variety of settings.

8 code implementations • ICLR 2018 • Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner

This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks.

no code implementations • NeurIPS 2017 • Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine

Off-policy model-free deep reinforcement learning methods using previously collected data can improve sample efficiency over on-policy policy gradient techniques.

no code implementations • ICLR 2018 • Matthias Bauer, Mateo Rojas-Carulla, Jakub Bartłomiej Świątkowski, Bernhard Schölkopf, Richard E. Turner

The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples.

1 code implementation • ICLR 2018 • Yingzhen Li, Richard E. Turner

Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real-world problems tackled by machine learning and a hot topic of current research.

3 code implementations • NeurIPS 2017 • Thang D. Bui, Cuong V. Nguyen, Richard E. Turner

Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped.

no code implementations • 27 Feb 2017 • Yingzhen Li, Richard E. Turner, Qiang Liu

We propose a novel approximate inference algorithm that approximates a target distribution by amortising the dynamics of a user-selected MCMC sampler.

no code implementations • ICML 2017 • Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck

This paper proposes a general method for improving the structure and quality of sequences generated by a recurrent neural network (RNN), while maintaining information originally learned from data, as well as sample diversity.

2 code implementations • 7 Nov 2016 • Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine

We analyze the connection between Q-Prop and existing model-free algorithms, and use control variate theory to derive two variants of Q-Prop with conservative and aggressive adaptation.

1 code implementation • 23 May 2016 • Thang D. Bui, Josiah Yan, Richard E. Turner

Unlike much of the previous venerable work in this area, the new framework is built on standard methods for approximate inference (variational free-energy, EP and Power EP methods) rather than employing approximations to the probabilistic generative model itself.

no code implementations • 16 Feb 2016 • Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner

First we introduce a new multivariate distribution over circular variables, called the multivariate Generalised von Mises (mGvM) distribution.

no code implementations • 12 Feb 2016 • Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers.

2 code implementations • NeurIPS 2016 • Yingzhen Li, Richard E. Turner

This paper introduces the variational R\'enyi bound (VR) that extends traditional variational inference to R\'enyi's alpha-divergences.

no code implementations • NeurIPS 2015 • Felipe Tobar, Thang D. Bui, Richard E. Turner

We introduce the Gaussian Process Convolution Model (GPCM), a two-stage nonparametric generative procedure to model stationary signals as the convolution between a continuous-time white-noise process and a continuous-time linear filter drawn from Gaussian process.

no code implementations • 11 Nov 2015 • Thang D. Bui, José Miguel Hernández-Lobato, Yingzhen Li, Daniel Hernández-Lobato, Richard E. Turner

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers.

3 code implementations • 10 Nov 2015 • José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Daniel Hernández-Lobato, Thang Bui, Richard E. Turner

Black-box alpha (BB-$\alpha$) is a new approximate inference method based on the minimization of $\alpha$-divergences.

no code implementations • 10 Nov 2015 • Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Thang Bui, Richard E. Turner

A method for large scale Gaussian process classification has been recently proposed based on expectation propagation (EP).

no code implementations • 20 Sep 2015 • Dan Stowell, Richard E. Turner

Training a denoising autoencoder neural network requires access to truly clean data, a requirement which is often impractical.

no code implementations • NeurIPS 2015 • Yingzhen Li, Jose Miguel Hernandez-Lobato, Richard E. Turner

Expectation propagation (EP) is a deterministic approximation algorithm that is often used to perform approximate Bayesian parameter learning.

no code implementations • NeurIPS 2015 • Shixiang Gu, Zoubin Ghahramani, Richard E. Turner

Experiments indicate that NASMC significantly improves inference in a non-linear state space model outperforming adaptive proposal methods including the Extended Kalman and Unscented Particle Filters.

no code implementations • 27 Apr 2015 • Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani

We then discuss augmented index sets and show that, contrary to previous works, marginal consistency of augmentation is not enough to guarantee consistency of variational inference with the original model.

no code implementations • NeurIPS 2014 • Thang D. Bui, Richard E. Turner

Gaussian process regression can be accelerated by constructing a small pseudo-dataset to summarise the observed data.

no code implementations • 23 Nov 2014 • Avid M. Afzal, Hamse Y. Mussa, Richard E. Turner, Andreas Bender, Robert C. Glen

According to Cobanoglu et al and Murphy, it is now widely acknowledged that the single target paradigm (one protein or target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable.

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