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05/26/2023
High-Fidelity Image Compression with Score-based Generative Models
Despite the tremendous success of diffusion generative models in text-to...
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01/26/2023
simple diffusion: End-to-end diffusion for high resolution images
Currently, applying diffusion models in pixel space of high resolution i...
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09/12/2022
Blurring Diffusion Models
Recently, Rissanen et al., (2022) have presented a new type of diffusion...
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03/31/2022
Equivariant Diffusion for Molecule Generation in 3D
This work introduces a diffusion model for molecule generation in 3D tha...
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10/05/2021
Autoregressive Diffusion Models
We introduce Autoregressive Diffusion Models (ARDMs), a model class enco...
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07/24/2021
Discrete Denoising Flows
Discrete flow-based models are a recently proposed class of generative m...
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05/19/2021
E(n) Equivariant Normalizing Flows for Molecule Generation in 3D
This paper introduces a generative model equivariant to Euclidean symmet...
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02/19/2021
E(n) Equivariant Graph Neural Networks
This paper introduces a new model to learn graph neural networks equivar...
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02/10/2021
Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models
The field of language modelling has been largely dominated by autoregres...
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12/24/2020
Variational Determinant Estimation with Spherical Normalizing Flows
This paper introduces the Variational Determinant Estimator (VDE), a var...
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11/14/2020
Self Normalizing Flows
Efficient gradient computation of the Jacobian determinant term is a cor...
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07/06/2020
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Normalizing flows and variational autoencoders are powerful generative m...
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06/02/2020
The Convolution Exponential and Generalized Sylvester Flows
This paper introduces a new method to build linear flows, by taking the ...
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02/23/2020
Predictive Sampling with Forecasting Autoregressive Models
Autoregressive models (ARMs) currently hold state-of-the-art performance...
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01/30/2020
Learning Discrete Distributions by Dequantization
Media is generally stored digitally and is therefore discrete. Many succ...
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11/29/2019
Learning Likelihoods with Conditional Normalizing Flows
Normalizing Flows (NFs) are able to model complicated distributions p(y)...
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05/17/2019
Integer Discrete Flows and Lossless Compression
Lossless compression methods shorten the expected representation size of...
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01/30/2019
Emerging Convolutions for Generative Normalizing Flows
Generative flows are attractive because they admit exact likelihood opti...
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03/06/2018