Fetching the paper…
Reading the bibliography…
Likelihood-based, or explicit, deep generative models use neural networks to construct flexible high-dimensional densities.
Estimation of a multivariate density
Theophilos Cacoullos · 1966
Earlier work this paper cites.
Treatise on Analysis: Volume III
Jean Dieudonné · 1973
Earlier work this paper cites.
The volume of a small geodesic ball of a riemannian manifold
Alfred Gray · 1974
Earlier work this paper cites.
Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1985
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
The MNIST database of handwritten digits, 1998
Y LeCun · 1998
Earlier work this paper cites.
Maximum likelihood estimation of intrinsic dimension
Elizaveta Levina and Peter Bickel · 2004
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
Earlier work this paper cites.
Pattern Recognition and Machine Learning
Christopher M. Bishop · 2006
Earlier work this paper cites.
Theory of point estimation
Erich L Lehmann and George Casella · 2006
Earlier work this paper cites.
Intrinsic statistics on riemannian manifolds: Basic tools for geometric measurements
Xavier Pennec · 2006
Earlier work this paper cites.
Matplotlib: A 2d graphics environment
J. D. Hunter · 2007
Earlier work this paper cites.
Probability and measure
Patrick Billingsley · 2008
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
Earlier work this paper cites.
Deep residual flow for out of distribution detection
Ev Zisselman and Aviv Tamar · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Submanifold density estimation
Arkadas Ozakin and Alexander Gray · 2009
Earlier work this paper cites.
Python 3 Reference Manual
Guido Van Rossum and Fred L. Drake · 2009
Earlier work this paper cites.
Sample complexity of testing the manifold hypothesis
Hariharan Narayanan and Sanjoy Mitter · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
Earlier work this paper cites.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Smooth manifolds
John M Lee · 2013
Earlier work this paper cites.
Rnade: The real-valued neural autoregressive density-estimator
Benigno Uria, Iain Murray, and Hugo Larochelle · 2013
Earlier work this paper cites.
What regularized auto-encoders learn from the data-generating distribution
Guillaume Alain and Yoshua Bengio · 2014
Earlier work this paper cites.
Amortized inference in probabilistic reasoning
Samuel Gershman and Noah Goodman · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Auto-encoding Variational Bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
Generalized autoencoder: A neural network framework for dimensionality reduction
Wei Wang, Yan Huang, Yizhou Wang, and Liang Wang · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Generative image modeling using spatial LSTMs
Lucas Theis and Matthias Bethge · 2015
Earlier work this paper cites.
Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2016
Earlier work this paper cites.
Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2016
Earlier work this paper cites.
Linear dynamical neural population models through nonlinear embeddings
Yuanjun Gao, Evan W Archer, Liam Paninski, and John P Cunningham · 2016
Earlier work this paper cites.
Normalizing flows on riemannian manifolds
Mevlana C Gemici, Danilo Rezende, and Shakir Mohamed · 2016
Earlier work this paper cites.
Jupyter notebooks – a publishing format for reproducible computational workflows
Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica Hamrick, Jason Grout, Sylvain Corlay, Paul Ivanov, Damián Avila, Safia Abdalla, and Carol Willing · 2016
Earlier work this paper cites.
Learning in implicit generative models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
Earlier work this paper cites.
F-GAN: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Lfads-latent factor analysis via dynamical systems
David Sussillo, Rafal Jozefowicz, LF Abbott, and Chethan Pandarinath · 2016
Earlier work this paper cites.
Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
Cited alongside, same era.
Mode regularized generative adversarial networks
Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li · 2017
Cited alongside, same era.
Variational lossy autoencoder
Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2017
Cited alongside, same era.
Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Cited alongside, same era.
Adversarial feature learning
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2017
Cited alongside, same era.
Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2017
Cited alongside, same era.
Flows for simultaneous manifold learning and density estimation
Johann Brehmer and Kyle Cranmer · 2020
Later among the works it cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Later among the works it cites.
Smoothness and stability in GANs
Casey Chu, Kentaro Minami, and Kenji Fukumizu · 2020
Later among the works it cites.
Relaxing bijectivity constraints with continuously indexed normalising flows
Rob Cornish, Anthony Caterini, George Deligiannidis, and Arnaud Doucet · 2020
Later among the works it cites.
nflows: normalizing flows in PyTorch, November 2020
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2020
Later among the works it cites.
From variational to deterministic autoencoders
Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Neural audio synthesis of musical notes with wavenet autoencoders
Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Mohammad Norouzi, Douglas Eck, and Karen Simonyan · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Cited alongside, same era.
GANs trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
Adversarial Variational Bayes: Unifying variational autoencoders and generative adversarial networks
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
Cited alongside, same era.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
Cited alongside, same era.
Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Later among the works it cites.
Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020
Later among the works it cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Later among the works it cites.
Transforming and projecting images into class-conditional generative networks
Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, and Aaron Hertzmann · 2020
Later among the works it cites.
Why normalizing flows fail to detect out-of-distribution data
Polina Kirichenko, Pavel Izmailov, and Andrew G Wilson · 2020
Later among the works it cites.
Perfect density models cannot guarantee anomaly detection
Charline Le Lan and Laurent Dinh · 2020
Later among the works it cites.
Enhancing scientific discoveries in molecular biology with deep generative models
Romain Lopez, Adam Gayoso, and Nir Yosef · 2020
Later among the works it cites.
Riemannian continuous normalizing flows
Emile Mathieu and Maximilian Nickel · 2020
Later among the works it cites.
Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2020
Later among the works it cites.
Sinkhorn autoencoders
Giorgio Patrini, Rianne van den Berg, Patrick Forre, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, and Frank Nielsen · 2020
Later among the works it cites.
Normalizing flows on tori and spheres
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racaniere, Michael Albergo, Gurtej Kanwar, Phiala Shanahan, and Kyle Cranmer · 2020
Later among the works it cites.
pytorch-fid: FID Score for PyTorch
Maximilian Seitzer · 2020
Later among the works it cites.
Yingfan Wang, Haiyang Huang, Cynthia Rudin, and Yaron Shaposhnik · 2020
Later among the works it cites.
Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2020
Later among the works it cites.
Generalized energy based models
Michael Arbel, Liang Zhou, and Arthur Gretton · 2021
Later among the works it cites.
Understanding and mitigating exploding inverses in invertible neural networks
Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger Grosse, and Jörn-Henrik Jacobsen · 2021
Later among the works it cites.
Deep generative modelling: A comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models
S Bond-Taylor, A Leach, Y Long, and CG Willcocks · 2021
Later among the works it cites.
Entropic Issues in Likelihood-Based OOD Detection
Anthony L Caterini and Gabriel Loaiza-Ganem · 2021
Later among the works it cites.
Rectangular flows for manifold learning
Anthony L Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, and John P Cunningham · 2021
Later among the works it cites.
Minwoo Chae, Dongha Kim, Yongdai Kim, and Lizhen Lin · 2021
Later among the works it cites.
A change of variables method for rectangular matrix-vector products
Edmond Cunningham and Madalina Fiterau · 2021
Later among the works it cites.
Lossless compression using continuously-indexed normalizing flows
Adam Golinski and Anthony L Caterini · 2021
Later among the works it cites.
functorch: Jax-like composable function transforms for pytorch
Horace He and Richard Zou · 2021
Later among the works it cites.
Variational diffusion models
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Later among the works it cites.
Representational aspects of depth and conditioning in normalizing flows
Frederic Koehler, Viraj Mehta, and Andrej Risteski · 2021
Later among the works it cites.
Trumpets: Injective flows for inference and inverse problems
Konik Kothari, AmirEhsan Khorashadizadeh, Maarten de Hoop, and Ivan Dokmanić · 2021
Later among the works it cites.
Improved autoregressive modeling with distribution smoothing
Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, and Stefano Ermon · 2021
Later among the works it cites.
Event generation and statistical sampling for physics with deep generative models and a density information buffer
Sydney Otten, Sascha Caron, Wieske de Swart, Melissa van Beekveld, Luc Hendriks, Caspar van Leeuwen, Damian Podareanu, Roberto Ruiz de Austri, and Rob Verheyen · 2021
Later among the works it cites.
Solving inverse problems using conditional invertible neural networks
Govinda Anantha Padmanabha and Nicholas Zabaras · 2021
Later among the works it cites.
The intrinsic dimension of images and its impact on learning
Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein · 2021
Later among the works it cites.
Skilful precipitation nowcasting using deep generative models of radar
Suman Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, et al · 2021
Later among the works it cites.
Tractable density estimation on learned manifolds with conformal embedding flows
Brendan Leigh Ross and Jesse C Cresswell · 2021
Later among the works it cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Later among the works it cites.
Image representations learned with unsupervised pre-training contain human-like biases
Ryan Steed and Aylin Caliskan · 2021
Later among the works it cites.
Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang · 2021
Later among the works it cites.
Pros and cons of gan evaluation measures: New developments
Ali Borji · 2022
Closest in time.
Magnet: Uniform sampling from deep generative network manifolds without retraining
Ahmed Imtiaz Humayun, Randall Balestriero, and Richard Baraniuk · 2022
Closest in time.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Closest in time.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Closest in time.
Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
Closest in time.
Dual use of artificial-intelligence-powered drug discovery
Fabio Urbina, Filippa Lentzos, Cédric Invernizzi, and Sean Ekins · 2022
Closest in time.
An introduction to neural data compression
Yibo Yang, Stephan Mandt, and Lucas Theis · 2022
Closest in time.