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Found 38 papers, 20 papers with code
GenCast: Diffusion-based ensemble forecasting for medium-range weather
Probabilistic weather forecasting is critical for decision-making in high-impact domains such as flood forecasting, energy system planning or transportation routing, where quantifying the uncertainty of a forecast -- including probabilities of extreme events -- is essential to guide important cost-benefit trade-offs and mitigation measures.
Understanding Deep Generative Models with Generalized Empirical Likelihoods
Understanding how well a deep generative model captures a distribution of high-dimensional data remains an important open challenge.
GraphCast: Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
se-Shweshwe Inspired Fashion Generation
Fashion is one of the ways in which we show ourselves to the world.
Skillful Precipitation Nowcasting using Deep Generative Models of Radar
To address these challenges, we present a Deep Generative Model for the probabilistic nowcasting of precipitation from radar.
Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities
Advances in algorithmic fairness have largely omitted sexual orientation and gender identity.
A case for new neural network smoothness constraints
How sensitive should machine learning models be to input changes?
A case for new neural networks smoothness constraints
How sensitive should machine learning models be to input changes?
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress.
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 very short term, typically less than two hours - a period in which traditional numerical weather prediction can be limited.
Levels of Analysis for Machine Learning
As a remedy for this dilemma, we advocate for the adoption of a common conceptual framework which can be used to understand, analyze, and discuss research.
Normalizing Flows for Probabilistic Modeling and Inference
In this review, we attempt to provide such a perspective by describing flows through the lens of probabilistic modeling and inference.
Monte Carlo Gradient Estimation in Machine Learning
This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the problem of computing the gradient of an expectation of a function with respect to parameters defining the distribution that is integrated; the problem of sensitivity analysis.
Training language GANs from Scratch
Generative Adversarial Networks (GANs) enjoy great success at image generation, but have proven difficult to train in the domain of natural language.
Learning Implicit Generative Models with the Method of Learned Moments
We propose a method of moments (MoM) algorithm for training large-scale implicit generative models.
Implicit Reparameterization Gradients
By providing a simple and efficient way of computing low-variance gradients of continuous random variables, the reparameterization trick has become the technique of choice for training a variety of latent variable models.
Unsupervised Predictive Memory in a Goal-Directed Agent
Animals execute goal-directed behaviours despite the limited range and scope of their sensors.
Distribution Matching in Variational Inference
With the increasingly widespread deployment of generative models, there is a mounting need for a deeper understanding of their behaviors and limitations.
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost.
Variational Approaches for Auto-Encoding Generative Adversarial Networks
In this paper, we develop a principle upon which auto-encoders can be combined with generative adversarial networks by exploiting the hierarchical structure of the generative model.
The Cramer Distance as a Solution to Biased Wasserstein Gradients
We show that the Cram\'er distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do.
Recurrent Environment Simulators
Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently.
Generative Temporal Models with Memory
We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations.
Normalizing Flows on Riemannian Manifolds
In spite of the multitude of algorithms available for density estimation in the Euclidean spaces $\mathbf{R}^n$ that scale to large n (e. g. normalizing flows, kernel methods and variational approximations), most of these methods are not immediately suitable for density estimation in more general Riemannian manifolds.
Learning in Implicit Generative Models
We frame GANs within the wider landscape of algorithms for learning in implicit generative models--models that only specify a stochastic procedure with which to generate data--and relate these ideas to modelling problems in related fields, such as econometrics and approximate Bayesian computation.
Unsupervised Learning of 3D Structure from Images
A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world.
Early Visual Concept Learning with Unsupervised Deep Learning
Automated discovery of early visual concepts from raw image data is a major open challenge in AI research.
One-Shot Generalization in Deep Generative Models
In particular, humans have an ability for one-shot generalization: an ability to encounter a new concept, understand its structure, and then be able to generate compelling alternative variations of the concept.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
The mutual information is a core statistical quantity that has applications in all areas of machine learning, whether this is in training of density models over multiple data modalities, in maximising the efficiency of noisy transmission channels, or when learning behaviour policies for exploration by artificial agents.
Variational Inference with Normalizing Flows
The choice of approximate posterior distribution is one of the core problems in variational inference.
Semi-Supervised Learning with Deep Generative Models
The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis.
Ranked #53 on
Image Classification
on SVHN
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning.
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression
We present a new variational inference algorithm for Gaussian processes with non-conjugate likelihood functions.
Expectation Propagation in Gaussian Process Dynamical Systems
Rich and complex time-series data, such as those generated from engineering sys- tems, financial markets, videos or neural recordings are now a common feature of modern data analysis.
Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version
Rich and complex time-series data, such as those generated from engineering systems, financial markets, videos or neural recordings, are now a common feature of modern data analysis.
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process
Nonparametric Bayesian models provide a framework for flexible probabilistic modelling of complex datasets.
Bayesian Exponential Family PCA
Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data.
