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06/16/2023
Understanding Deep Generative Models with Generalized Empirical Likelihoods
Understanding how well a deep generative model captures a distribution o...
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12/24/2022
GraphCast: Learning skillful medium-range global weather forecasting
We introduce a machine-learning (ML)-based weather simulator–called "Gra...
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09/15/2022
Power to the People? Opportunities and Challenges for Participatory AI
Participatory approaches to artificial intelligence (AI) and machine lea...
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02/25/2022
se-Shweshwe Inspired Fashion Generation
Fashion is one of the ways in which we show ourselves to the world. It i...
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04/02/2021
Skillful Precipitation Nowcasting using Deep Generative Models of Radar
Precipitation nowcasting, the high-resolution forecasting of precipitati...
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02/03/2021
Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities
Advances in algorithmic fairness have largely omitted sexual orientation...
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12/14/2020
A case for new neural network smoothness constraints
How sensitive should machine learning models be to input changes? We tac...
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07/08/2020
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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05/11/2020
A review of radar-based 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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04/06/2020
Levels of Analysis for Machine Learning
Machine learning is currently involved in some of the most vigorous deba...
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12/05/2019
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing flows provide a general mechanism for defining expressive pr...
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06/25/2019
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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05/23/2019
Training language GANs from Scratch
Generative Adversarial Networks (GANs) enjoy great success at image gene...
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06/28/2018
Learning Implicit Generative Models with the Method of Learned Moments
We propose a method of moments (MoM) algorithm for training large-scale ...
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05/22/2018
Implicit Reparameterization Gradients
By providing a simple and efficient way of computing low-variance gradie...
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03/28/2018
Unsupervised Predictive Memory in a Goal-Directed Agent
Animals execute goal-directed behaviours despite the limited range and s...
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02/19/2018
Distribution Matching in Variational Inference
The difficulties in matching the latent posterior to the prior, balancin...
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10/23/2017
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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06/15/2017
Variational Approaches for Auto-Encoding Generative Adversarial Networks
Auto-encoding generative adversarial networks (GANs) combine the standar...
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05/30/2017
The Cramer Distance as a Solution to Biased Wasserstein Gradients
The Wasserstein probability metric has received much attention from the ...
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04/07/2017
Recurrent Environment Simulators
Models that can simulate how environments change in response to actions ...
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02/15/2017
Generative Temporal Models with Memory
We consider the general problem of modeling temporal data with long-rang...
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11/07/2016
Normalizing Flows on Riemannian Manifolds
We consider the problem of density estimation on Riemannian manifolds. D...
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10/11/2016
Learning in Implicit Generative Models
Generative adversarial networks (GANs) provide an algorithmic framework ...
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07/03/2016
Unsupervised Learning of 3D Structure from Images
A key goal of computer vision is to recover the underlying 3D structure ...
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06/17/2016
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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03/16/2016
One-Shot Generalization in Deep Generative Models
Humans have an impressive ability to reason about new concepts and exper...
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09/29/2015
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
The mutual information is a core statistical quantity that has applicati...
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05/21/2015
Variational Inference with Normalizing Flows
The choice of approximate posterior distribution is one of the core prob...
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01/16/2014
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
We marry ideas from deep neural networks and approximate Bayesian infere...
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07/12/2012
Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version
Rich and complex time-series data, such as those generated from engineer...
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06/06/2011