
E(n) Equivariant Normalizing Flows for Molecule Generation in 3D
This paper introduces a generative model equivariant to Euclidean symmet...
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E(n) Equivariant Graph Neural Networks
This paper introduces a new model to learn graph neural networks equivar...
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Argmax Flows and Multinomial Diffusion: Towards NonAutoregressive Language Models
The field of language modelling has been largely dominated by autoregres...
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Variational Determinant Estimation with Spherical Normalizing Flows
This paper introduces the Variational Determinant Estimator (VDE), a var...
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Self Normalizing Flows
Efficient gradient computation of the Jacobian determinant term is a cor...
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SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Normalizing flows and variational autoencoders are powerful generative m...
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The Convolution Exponential and Generalized Sylvester Flows
This paper introduces a new method to build linear flows, by taking the ...
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Predictive Sampling with Forecasting Autoregressive Models
Autoregressive models (ARMs) currently hold stateoftheart performance...
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Learning Discrete Distributions by Dequantization
Media is generally stored digitally and is therefore discrete. Many succ...
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Learning Likelihoods with Conditional Normalizing Flows
Normalizing Flows (NFs) are able to model complicated distributions p(y)...
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Integer Discrete Flows and Lossless Compression
Lossless compression methods shorten the expected representation size of...
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Emerging Convolutions for Generative Normalizing Flows
Generative flows are attractive because they admit exact likelihood opti...
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HexaConv
The effectiveness of Convolutional Neural Networks stems in large part f...
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Emiel Hoogeboom
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