
Implicit Riemannian Concave Potential Maps
We are interested in the challenging problem of modelling densities on R...
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Flowbased sampling for fermionic lattice field theories
Algorithms based on normalizing flows are emerging as promising machine ...
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NeRFVAE: A Geometry Aware 3D Scene Generative Model
We propose NeRFVAE, a 3D scene generative model that incorporates geome...
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Amortized learning of neural causal representations
Causal models can compactly and efficiently encode the datagenerating p...
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Conditional Set Generation with Transformers
A set is an unordered collection of unique elements–and yet many machine...
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Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations a...
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Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
Inspired by recent work in attention models for image captioning and que...
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Consistent Generative Query Networks
Stochastic video prediction is usually framed as an extrapolation proble...
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Learning models for visual 3D localization with implicit mapping
We propose a formulation of visual localization that does not require co...
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Neural Processes
A neural network (NN) is a parameterised function that can be tuned via ...
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Conditional Neural Processes
Deep neural networks excel at function approximation, yet they are typic...
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Optimizing Slate Recommendations via SlateCVAE
The slate recommendation problem aims to find the "optimal" ordering of ...
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Generative Temporal Models with Memory
We consider the general problem of modeling temporal data with longrang...
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Variational inference for Monte Carlo objectives
Recent progress in deep latent variable models has largely been driven b...
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Danilo J. Rezende
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