
Introduction to Normalizing Flows for Lattice Field Theory
This notebook tutorial demonstrates a method for sampling Boltzmann dist...
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Sampling using SU(N) gauge equivariant flows
We develop a flowbased sampling algorithm for SU(N) lattice gauge theor...
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Neural Communication Systems with Bandwidthlimited Channel
Reliably transmitting messages despite information loss due to a noisy c...
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Equivariant flowbased sampling for lattice gauge theory
We define a class of machinelearned flowbased sampling algorithms for ...
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Targeted free energy estimation via learned mappings
Free energy perturbation (FEP) was proposed by Zwanzig more than six dec...
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Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributi...
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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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Information bottleneck through variational glasses
Information bottleneck (IB) principle [1] has become an important elemen...
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Hamiltonian Generative Networks
The Hamiltonian formalism plays a central role in classical and quantum ...
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Equivariant Hamiltonian Flows
This paper introduces equivariant hamiltonian flows, a method for learni...
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Shaping Belief States with Generative Environment Models for RL
When agents interact with a complex environment, they must form and main...
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A Hierarchical Probabilistic UNet for Modeling MultiScale Ambiguities
Medical imaging only indirectly measures the molecular identity of the t...
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Taming VAEs
In spite of remarkable progress in deep latent variable generative model...
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A Probabilistic UNet for Segmentation of Ambiguous Images
Many realworld vision problems suffer from inherent ambiguities. In cli...
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Generative Temporal Models with Spatial Memory for Partially Observed Environments
In modelbased reinforcement learning, generative and temporal models of...
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ImaginationAugmented Agents for Deep Reinforcement Learning
We introduce ImaginationAugmented Agents (I2As), a novel architecture f...
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Variational Intrinsic Control
In this paper we introduce a new unsupervised reinforcement learning met...
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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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Towards Conceptual Compression
We introduce a simple recurrent variational autoencoder architecture th...
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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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DRAW: A Recurrent Neural Network For Image Generation
This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural ...
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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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Danilo Jimenez Rezende
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