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MaCow: Masked Convolutional Generative Flow

MaCow: Masked Convolutional Generative Flow

12 February 2019
Xuezhe Ma
Xiang Kong
Shanghang Zhang
Eduard H. Hovy
    DRL
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Papers citing "MaCow: Masked Convolutional Generative Flow"

16 / 16 papers shown
Title
Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing Flows
Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing Flows
Sandeep Nagar
Girish Varma
TPM
29
0
0
18 Oct 2024
Training Energy-Based Normalizing Flow with Score-Matching Objectives
Training Energy-Based Normalizing Flow with Score-Matching Objectives
Chen-Hao Chao
Wei-Fang Sun
Yen-Chang Hsu
Z. Kira
Chun-Yi Lee
33
3
0
24 May 2023
FInC Flow: Fast and Invertible $k \times k$ Convolutions for Normalizing
  Flows
FInC Flow: Fast and Invertible k×kk \times kk×k Convolutions for Normalizing Flows
Aditya Kallappa
Sandeep Nagar
Girish Varma
27
2
0
23 Jan 2023
Efficient-VDVAE: Less is more
Efficient-VDVAE: Less is more
Louay Hazami
Rayhane Mama
Ragavan Thurairatnam
BDL
29
28
0
25 Mar 2022
Resampling Base Distributions of Normalizing Flows
Resampling Base Distributions of Normalizing Flows
Vincent Stimper
Bernhard Schölkopf
José Miguel Hernández-Lobato
BDL
30
32
0
29 Oct 2021
PixelPyramids: Exact Inference Models from Lossless Image Pyramids
PixelPyramids: Exact Inference Models from Lossless Image Pyramids
Shweta Mahajan
Stefan Roth
TPM
15
2
0
17 Oct 2021
Deep Generative Modelling: A Comparative Review of VAEs, GANs,
  Normalizing Flows, Energy-Based and Autoregressive Models
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
Sam Bond-Taylor
Adam Leach
Yang Long
Chris G. Willcocks
VLM
TPM
45
485
0
08 Mar 2021
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Jason J. Yu
Konstantinos G. Derpanis
Marcus A. Brubaker
TPM
32
41
0
26 Oct 2020
Fast, Accurate, and Simple Models for Tabular Data via Augmented
  Distillation
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Rasool Fakoor
Jonas W. Mueller
Nick Erickson
Pratik Chaudhari
Alex Smola
26
54
0
25 Jun 2020
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows
  and Latent Variable Models
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
Chin-Wei Huang
Laurent Dinh
Aaron Courville
DRL
31
87
0
17 Feb 2020
Learning Discrete Distributions by Dequantization
Learning Discrete Distributions by Dequantization
Emiel Hoogeboom
Taco S. Cohen
Jakub M. Tomczak
DRL
34
31
0
30 Jan 2020
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios
Eric T. Nalisnick
Danilo Jimenez Rezende
S. Mohamed
Balaji Lakshminarayanan
TPM
AI4CE
62
1,635
0
05 Dec 2019
The Neural Moving Average Model for Scalable Variational Inference of
  State Space Models
The Neural Moving Average Model for Scalable Variational Inference of State Space Models
Tom Ryder
D. Prangle
Andrew Golightly
Isaac Matthews
BDL
AI4TS
16
6
0
02 Oct 2019
Structured Output Learning with Conditional Generative Flows
Structured Output Learning with Conditional Generative Flows
You Lu
Bert Huang
BDL
DRL
21
72
0
30 May 2019
HINT: Hierarchical Invertible Neural Transport for Density Estimation
  and Bayesian Inference
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
Jakob Kruse
Gianluca Detommaso
Ullrich Kothe
Robert Scheichl
13
45
0
25 May 2019
Pixel Recurrent Neural Networks
Pixel Recurrent Neural Networks
Aaron van den Oord
Nal Kalchbrenner
Koray Kavukcuoglu
SSeg
GAN
272
2,553
0
25 Jan 2016
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