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04/11/2022
Learning to Induce Causal Structure
The fundamental challenge in causal induction is to infer the underlying...
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01/20/2021
Introduction to Normalizing Flows for Lattice Field Theory
This notebook tutorial demonstrates a method for sampling Boltzmann dist...
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08/12/2020
Sampling using SU(N) gauge equivariant flows
We develop a flow-based sampling algorithm for SU(N) lattice gauge theor...
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03/30/2020
Neural Communication Systems with Bandwidth-limited Channel
Reliably transmitting messages despite information loss due to a noisy c...
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03/13/2020
Equivariant flow-based sampling for lattice gauge theory
We define a class of machine-learned flow-based sampling algorithms for ...
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02/12/2020
Targeted free energy estimation via learned mappings
Free energy perturbation (FEP) was proposed by Zwanzig more than six dec...
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02/06/2020
Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributi...
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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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12/02/2019
Information bottleneck through variational glasses
Information bottleneck (IB) principle [1] has become an important elemen...
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09/30/2019
Hamiltonian Generative Networks
The Hamiltonian formalism plays a central role in classical and quantum ...
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09/30/2019
Equivariant Hamiltonian Flows
This paper introduces equivariant hamiltonian flows, a method for learni...
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06/21/2019
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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05/30/2019
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Medical imaging only indirectly measures the molecular identity of the t...
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10/01/2018
Taming VAEs
In spite of remarkable progress in deep latent variable generative model...
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06/13/2018
A Probabilistic U-Net for Segmentation of Ambiguous Images
Many real-world vision problems suffer from inherent ambiguities. In cli...
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04/25/2018
Generative Temporal Models with Spatial Memory for Partially Observed Environments
In model-based reinforcement learning, generative and temporal models of...
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07/19/2017
Imagination-Augmented Agents for Deep Reinforcement Learning
We introduce Imagination-Augmented Agents (I2As), a novel architecture f...
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11/22/2016
Variational Intrinsic Control
In this paper we introduce a new unsupervised reinforcement learning met...
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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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04/29/2016
Towards Conceptual Compression
We introduce a simple recurrent variational auto-encoder architecture th...
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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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02/16/2015
DRAW: A Recurrent Neural Network For Image Generation
This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural ...
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01/16/2014