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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.

no code implementations • 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.

no code implementations • 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.

no code implementations • 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.

2 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.

2 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.

1 code implementation • 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.

6 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 • 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.

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.

2 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.

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.

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.

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).

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 • 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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