
Skillful Precipitation Nowcasting using Deep Generative Models of Radar
Precipitation nowcasting, the highresolution forecasting of precipitati...
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Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities
Advances in algorithmic fairness have largely omitted sexual orientation...
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A case for new neural network smoothness constraints
How sensitive should machine learning models be to input changes? We tac...
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Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
This paper explores the important role of critical science, and in parti...
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A review of radarbased nowcasting of precipitation and applicable machine learning techniques
A 'nowcast' is a type of weather forecast which makes predictions in the...
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Levels of Analysis for Machine Learning
Machine learning is currently involved in some of the most vigorous deba...
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Normalizing Flows for Probabilistic Modeling and Inference
Normalizing flows provide a general mechanism for defining expressive pr...
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Monte Carlo Gradient Estimation in Machine Learning
This paper is a broad and accessible survey of the methods we have at ou...
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Training language GANs from Scratch
Generative Adversarial Networks (GANs) enjoy great success at image gene...
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Learning Implicit Generative Models with the Method of Learned Moments
We propose a method of moments (MoM) algorithm for training largescale ...
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Implicit Reparameterization Gradients
By providing a simple and efficient way of computing lowvariance gradie...
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Unsupervised Predictive Memory in a GoalDirected Agent
Animals execute goaldirected behaviours despite the limited range and s...
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Distribution Matching in Variational Inference
The difficulties in matching the latent posterior to the prior, balancin...
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Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
Generative adversarial networks (GANs) are a family of generative models...
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Variational Approaches for AutoEncoding Generative Adversarial Networks
Autoencoding generative adversarial networks (GANs) combine the standar...
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The Cramer Distance as a Solution to Biased Wasserstein Gradients
The Wasserstein probability metric has received much attention from the ...
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Recurrent Environment Simulators
Models that can simulate how environments change in response to actions ...
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Generative Temporal Models with Memory
We consider the general problem of modeling temporal data with longrang...
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Normalizing Flows on Riemannian Manifolds
We consider the problem of density estimation on Riemannian manifolds. D...
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Learning in Implicit Generative Models
Generative adversarial networks (GANs) provide an algorithmic framework ...
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Unsupervised Learning of 3D Structure from Images
A key goal of computer vision is to recover the underlying 3D structure ...
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Early Visual Concept Learning with Unsupervised Deep Learning
Automated discovery of early visual concepts from raw image data is a ma...
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OneShot Generalization in Deep Generative Models
Humans have an impressive ability to reason about new concepts and exper...
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Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
The mutual information is a core statistical quantity that has applicati...
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Variational Inference with Normalizing Flows
The choice of approximate posterior distribution is one of the core prob...
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
We marry ideas from deep neural networks and approximate Bayesian infere...
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Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version
Rich and complex timeseries data, such as those generated from engineer...
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Bayesian and L1 Approaches to Sparse Unsupervised Learning
The use of L1 regularisation for sparse learning has generated immense r...
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Shakir Mohamed
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Research Scientist at Google DeepMind since 2014, Senior Researcher in Statistical Machine Learning at DeepMind Technologies from 20132014, Junior Research Fellow at the University of British Columbia at Canadian Institute For Advanced Research from 20112013, Business Analyst at Nedbank from 20062007, Postdoctoral researcher in Machine Learning at the University of British Columbia in the Department of Computer Science, Postdoctoral researcher at Laboratory for Computational Intelligence (LCI), Junior fellowship from the Canadian Institute for Advanced Research (CIFAR), within the Neural Computation and Adaptive Perception (NCAP), PhD in the Machine Learning Group at the University of Cambridge Commonwealth Scholar to the United Kingdom and a member of St John's College.