Fetching the paper…
Reading the bibliography…
Recent years have witnessed a surge in the popularity of Machine Learning (ML), applied across diverse domains.
Estimating the dimension of a model
Gideon Schwarz · 1978
Earlier work this paper cites.
Information processing in dynamical systems: Foundations of harmony theory
Paul Smolensky · 1986
Earlier work this paper cites.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Rprop-description and implementation details
Martin Riedmiller and I Rprop · 1994
Earlier work this paper cites.
The helmholtz machine
Peter Dayan, Geoffrey E Hinton, Radford M Neal, and Richard S Zemel · 1995
Earlier work this paper cites.
The" wake-sleep" algorithm for unsupervised neural networks
Geoffrey E Hinton, Peter Dayan, Brendan J Frey, and Radford M Neal · 1995
Earlier work this paper cites.
Biological sequence analysis: probabilistic models of proteins and nucleic acids
Richard Durbin, Sean R Eddy, Anders Krogh, and Graeme Mitchison · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent · 2000
Earlier work this paper cites.
An introduction to bayesian network theory and usage
Todd Andrew Stephenson · 2000
Earlier work this paper cites.
Causation, prediction, and search
Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman · 2000
Earlier work this paper cites.
Using multiattribute prediction suffix graphs to predict and generate music
José Luis Triviño-Rodriguez and Rafael Morales-Bueno · 2001
Earlier work this paper cites.
A survey of algorithms for real-time bayesian network inference
Haipeng Guo and William Hsu · 2002
Earlier work this paper cites.
deal: A package for learning bayesian networks
Susanne G Bøttcher and Claus Dethlefsen · 2003
Earlier work this paper cites.
Beyond the cybernetic jam fantasy: The continuator
François Pachet · 2004
Earlier work this paper cites.
Exact bayesian structure discovery in bayesian networks
Mikko Koivisto and Kismat Sood · 2004
Earlier work this paper cites.
Learning methods for generic object recognition with invariance to pose and lighting
Yann LeCun, Fu Jie Huang, and Leon Bottou · 2004
Earlier work this paper cites.
Sparse coding of sensory inputs
Bruno A Olshausen and David J Field · 2004
Earlier work this paper cites.
Finding optimal Bayesian networks by dynamic programming
Ajit P Singh and Andrew W Moore · 2005
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh · 2006
Earlier work this paper cites.
A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
Earlier work this paper cites.
Learning multilevel distributed representations for high-dimensional sequences
Ilya Sutskever and Geoffrey Hinton · 2007
Earlier work this paper cites.
Unsupervised learning of image transformations
Roland Memisevic and Geoffrey Hinton · 2007
Earlier work this paper cites.
The recurrent temporal restricted boltzmann machine
Ilya Sutskever, Geoffrey E Hinton, and Graham W Taylor · 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.
Offline handwriting recognition with multidimensional recurrent neural networks
Alex Graves and Jürgen Schmidhuber · 2008
Earlier work this paper cites.
Using bayesian networks to create synthetic data
Jim Young, Patrick Graham, and Richard Penny · 2009
Earlier work this paper cites.
Multiply imputed synthetic data: Evaluation of hierarchical bayesian imputation models
Patrick Graham, Jim Young, and Richard Penny · 2009
Earlier work this paper cites.
Deep boltzmann machines
Ruslan Salakhutdinov and Geoffrey Hinton · 2009
Earlier work this paper cites.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y Ng · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning to create jazz melodies using deep belief nets
Greg Bickerman, Sam Bosley, Peter Swire, and Robert M Keller · 2010
Earlier work this paper cites.
Learning to represent spatial transformations with factored higher-order boltzmann machines
Roland Memisevic and Geoffrey E Hinton · 2010
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and Léon Bottou · 2010
Earlier work this paper cites.
Learning optimal bayesian networks using a* search
Changhe Yuan, Brandon Malone, and Xiaojian Wu · 2011
Earlier work this paper cites.
Two distributed-state models for generating high-dimensional time series
Graham W Taylor, Geoffrey E Hinton, and Sam T Roweis · 2011
Earlier work this paper cites.
Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011
Earlier work this paper cites.
Gradient-based learning of higher-order image features
Roland Memisevic · 2011
Earlier work this paper cites.
The neural autoregressive distribution estimator
Hugo Larochelle and Iain Murray · 2011
Earlier work this paper cites.
Quickly generating representative samples from an rbm-derived process
Olivier Breuleux, Yoshua Bengio, and Pascal Vincent · 2011
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.
Markov constraints for generating lyrics with style
Gabriele Barbieri, François Pachet, Pierre Roy, and Mirko Degli Esposti · 2012
Earlier work this paper cites.
A simple approach for finding the globally optimal bayesian network structure
Tomi Silander and Petri Myllymaki · 2012
Earlier work this paper cites.
Polyphonic accompaniment using genetic algorithm with music theory
Chien-Hung Liu and Chuan-Kang Ting · 2012
Earlier work this paper cites.
A generative process for sampling contractive auto-encoders
Salah Rifai, Yoshua Bengio, Yann Dauphin, and Pascal Vincent · 2012
Earlier work this paper cites.
Nicolas Boulanger-Lewandowski, Yoshua Bengio, and Pascal Vincent · 2012
Earlier work this paper cites.
Advances in optimizing recurrent networks
Yoshua Bengio, Nicolas Boulanger-Lewandowski, and Razvan Pascanu · 2012
Earlier work this paper cites.
Subword language modeling with neural networks
Tomáš Mikolov, Ilya Sutskever, Anoop Deoras, Hai-Son Le, Stefan Kombrink, and Jan Cernocky · 2012
Earlier work this paper cites.
Mixtures of conditional gaussian scale mixtures applied to multiscale image representations
Lucas Theis, Reshad Hosseini, and Matthias Bethge · 2012
Earlier work this paper cites.
A naturalistic open source movie for optical flow evaluation
Daniel J Butler, Jonas Wulff, Garrett B Stanley, and Michael J Black · 2012
Earlier work this paper cites.
Towards a simulation driven stereo vision system
Martin Peris, Sara Martull, Atsuto Maki, Yasuhiro Ohkawa, and Kazuhiro Fukui · 2012
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2012
Earlier work this paper cites.
Melody harmonization with interpolated probabilistic models
Stanisław A Raczyński, Satoru Fukayama, and Emmanuel Vincent · 2013
Earlier work this paper cites.
Enforcing meter in finite-length markov sequences
Pierre Roy and François Pachet · 2013
Earlier work this paper cites.
Generating musical accompaniment using finite state transducers
Jonathan P Forsyth and Juan P Bello · 2013
Earlier work this paper cites.
Fast gradient-based inference with continuous latent variable models in auxiliary form
Diederik P Kingma · 2013
Earlier work this paper cites.
Generalized denoising auto-encoders as generative models, 2013
Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent · 2013
Earlier work this paper cites.
Better mixing via deep representations
Yoshua Bengio, Grégoire Mesnil, Yann Dauphin, and Salah Rifai · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 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.
Generating sequences with recurrent neural networks
Alex Graves · 2013
Earlier work this paper cites.
Computer-aided music composition with lstm neural network and chaotic inspiration
Andrés E Coca, Débora C Corrêa, and Liang Zhao · 2013
Earlier work this paper cites.
On fast dropout and its applicability to recurrent networks
Justin Bayer, Christian Osendorfer, Daniela Korhammer, Nutan Chen, Sebastian Urban, and Patrick van der Smagt · 2013
Earlier work this paper cites.
How to construct deep recurrent neural networks
Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio · 2013
Earlier work this paper cites.
Framework for generation of synthetic ground truth data for driver assistance applications
Vladimir Haltakov, Christian Unger, and Slobodan Ilic · 2013
Earlier work this paper cites.
Ai methods in algorithmic composition: A comprehensive survey
Jose D Fernández and Francisco Vico · 2013
Earlier work this paper cites.
Factoring variations in natural images with deep gaussian mixture models
Aaron Van den Oord and Benjamin Schrauwen · 2014
Earlier work this paper cites.
Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis
Heiga Zen and Andrew Senior · 2014
Earlier work this paper cites.
Probabilistic harmonization with fixed intermediate chord constraints
Maximos Kaliakatsos-Papakostas and Emilios Cambouropoulos · 2014
Earlier work this paper cites.
Video (language) modeling: a baseline for generative models of natural videos
MarcAurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, and Sumit Chopra · 2014
Earlier work this paper cites.
Avoiding plagiarism in markov sequence generation
Alexandre Papadopoulos, Pierre Roy, and François Pachet · 2014
Earlier work this paper cites.
Four-part harmonization using bayesian networks: pros and cons of introducing chord nodes
Syunpei Suzuki and Tetsuro Kitahara · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Structured recurrent temporal restricted boltzmann machines
Roni Mittelman, Benjamin Kuipers, Silvio Savarese, and Honglak Lee · 2014
Earlier work this paper cites.
Musical audio synthesis using autoencoding neural nets
Andy Sarroff and Michael A Casey · 2014
Earlier work this paper cites.
Deep autoregressive networks
Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, and Daan Wierstra · 2014
Earlier work this paper cites.
Deep generative stochastic networks trainable by backprop
Yoshua Bengio, Eric Laufer, Guillaume Alain, and Jason Yosinski · 2014
Earlier work this paper cites.
Variational recurrent auto-encoders
Otto Fabius and Joost R Van Amersfoort · 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.
Modeling deep temporal dependencies with recurrent grammar cells""
Vincent Michalski, Roland Memisevic, and Kishore Konda · 2014
Earlier work this paper cites.
A deep and tractable density estimator
Benigno Uria, Iain Murray, and Hugo Larochelle · 2014
Earlier work this paper cites.
Iterative neural autoregressive distribution estimator nade-k
Tapani Raiko, Yao Li, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Translating videos to natural language using deep recurrent neural networks
Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, and Kate Saenko · 2014
Earlier work this paper cites.
Chinese poetry generation with recurrent neural networks
Xingxing Zhang and Mirella Lapata · 2014
Earlier work this paper cites.
Learning stochastic recurrent networks
Justin Bayer and Christian Osendorfer · 2014
Earlier work this paper cites.
A clockwork rnn
Jan Koutnik, Klaus Greff, Faustino Gomez, and Juergen Schmidhuber · 2014
Earlier work this paper cites.
Bach in 2014: Music composition with recurrent neural network
I Liu, Bhiksha Ramakrishnan, et al · 2014
Earlier work this paper cites.
Polyphonic music generation by modeling temporal dependencies using a rnn-dbn
Kratarth Goel, Raunaq Vohra, and Jajati Keshari Sahoo · 2014
Earlier work this paper cites.
Deep captioning with multimodal recurrent neural networks (m-rnn)
Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, and Alan Yuille · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 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.
Learning a deep convolutional network for image super-resolution
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
Conditional generative adversarial nets for convolutional face generation
Jon Gauthier · 2014
Earlier work this paper cites.
Differentially private synthesization of multi-dimensional data using copula functions
Haoran Li, Li Xiong, and Xiaoqian Jiang · 2014
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
A benchmark for rgb-d visual odometry, 3d reconstruction and slam
Ankur Handa, Thomas Whelan, John McDonald, and Andrew J Davison · 2014
Earlier work this paper cites.
A natural and synthetic corpus for benchmarking of hand gesture recognition systems
Javier Molina, José A Pajuelo, Marcos Escudero-Viñolo, Jesús Bescós, and José M Martínez · 2014
Earlier work this paper cites.
From virtual to reality: Fast adaptation of virtual object detectors to real domains
Baochen Sun and Kate Saenko · 2014
Earlier work this paper cites.
Image-based synthesis and re-synthesis of viewpoints guided by 3d models
Konstantinos Rematas, Tobias Ritschel, Mario Fritz, and Tinne Tuytelaars · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Deep belief networks and deep learning
Yuming Hua, Junhai Guo, and Hua Zhao · 2015
Earlier work this paper cites.
A hierarchical neural autoencoder for paragraphs and documents
Jiwei Li, Minh-Thang Luong, and Dan Jurafsky · 2015
Earlier work this paper cites.
Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
Earlier work this paper cites.
Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra · 2015
Earlier work this paper cites.
Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
Earlier work this paper cites.
Deep convolutional inverse graphics network
Tejas D Kulkarni, William F Whitney, Pushmeet Kohli, and Josh Tenenbaum · 2015
Earlier work this paper cites.
Denoising criterion for variational auto-encoding framework
Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic, and Yoshua Bengio · 2015
Earlier work this paper cites.
Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 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 · 2015
Earlier work this paper cites.
Efficient inference in occlusion-aware generative models of images
Jonathan Huang and Kevin Murphy · 2015
Earlier work this paper cites.
Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
Earlier work this paper cites.
Deep networks for image super-resolution with sparse prior
Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, and Thomas Huang · 2015
Earlier work this paper cites.
A critical review of recurrent neural networks for sequence learning
Zachary Lipton · 2015
Earlier work this paper cites.
Show and tell: A neural image caption generator
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan · 2015
Earlier work this paper cites.
Long-term recurrent convolutional networks for visual recognition and description
Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell · 2015
Earlier work this paper cites.
Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
Earlier work this paper cites.
Generating images from captions with attention
Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
Generative image modeling using spatial lstms
Lucas Theis and Matthias Bethge · 2015
Earlier work this paper cites.
Bidirectional recurrent neural networks as generative models
Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek, and Juha T Karhunen · 2015
Earlier work this paper cites.
A recurrent latent variable model for sequential data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio · 2015
Earlier work this paper cites.
Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
Earlier work this paper cites.
Mind’s eye: A recurrent visual representation for image caption generation
Xinlei Chen and C Lawrence Zitnick · 2015
Earlier work this paper cites.
Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei · 2015
Earlier work this paper cites.
Modeling temporal dependencies in data using a dbn-lstm
Raunaq Vohra, Kratarth Goel, and Jajati Keshari Sahoo · 2015
Earlier work this paper cites.
Modelling high-dimensional sequences with lstm-rtrbm: Application to polyphonic music generation
Qi Lyu, Zhiyong Wu, Jun Zhu, and Helen Meng · 2015
Earlier work this paper cites.
Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
Earlier work this paper cites.
Sequence to sequence-video to text
Subhashini Venugopalan, Marcus Rohrbach, Jeffrey Donahue, Raymond Mooney, Trevor Darrell, and Kate Saenko · 2015
Earlier work this paper cites.
Describing videos by exploiting temporal structure
Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, and Aaron Courville · 2015
Earlier work this paper cites.
Training very deep networks
Rupesh K Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
Earlier work this paper cites.
Unsupervised learning of visual structure using predictive generative networks
William Lotter, Gabriel Kreiman, and David Cox · 2015
Earlier work this paper cites.
Super-resolution with deep convolutional sufficient statistics
Joan Bruna, Pablo Sprechmann, and Yann LeCun · 2015
Earlier work this paper cites.
Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2015
Earlier work this paper cites.
Deepdream-a code example for visualizing neural networks
Alexander Mordvintsev, Christopher Olah, and Mike Tyka · 2015
Earlier work this paper cites.
Inceptionism: Going deeper into neural networks
Alexander Mordvintsev, Christopher Olah, and Mike Tyka · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2015
Earlier work this paper cites.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 2015
Earlier work this paper cites.
Deep generative image models using a laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 2015
Earlier work this paper cites.
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
Earlier work this paper cites.
Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 2015
Earlier work this paper cites.
Training generative neural networks via maximum mean discrepancy optimization
Gintare Karolina Dziugaite, Daniel M Roy, and Zoubin Ghahramani · 2015
Earlier work this paper cites.
Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
Hao Su, Charles R Qi, Yangyan Li, and Leonidas J Guibas · 2015
Earlier work this paper cites.
Learning deep object detectors from 3d models
Xingchao Peng, Baochen Sun, Karim Ali, and Kate Saenko · 2015
Earlier work this paper cites.
Semantic pose using deep networks trained on synthetic rgb-d
Jeremie Papon and Markus Schoeler · 2015
Earlier work this paper cites.
Flownet: Learning optical flow with convolutional networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick Van der Smagt, Daniel Cremers, and Thomas Brox · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Zinc 15 – ligand discovery for everyone
Teague Sterling and John J. Irwin · 2015
Earlier work this paper cites.
Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
Earlier work this paper cites.
Python data science handbook: Essential tools for working with data
Jake VanderPlas · 2016
Earlier work this paper cites.
Synthesizing plausible privacy-preserving location traces
Vincent Bindschaedler and Reza Shokri · 2016
Earlier work this paper cites.
Assisted lead sheet composition using flowcomposer
Alexandre Papadopoulos, Pierre Roy, and François Pachet · 2016
Earlier work this paper cites.
Music generation from statistical models of harmony
Raymond P Whorley and Darrell Conklin · 2016
Earlier work this paper cites.
Synthesizing training images for boosting human 3d pose estimation
Wenzheng Chen, Huan Wang, Yangyan Li, Hao Su, Zhenhua Wang, Changhe Tu, Dani Lischinski, Daniel Cohen-Or, and Baoquan Chen · 2016
Earlier work this paper cites.
Automatic chord generation system using basic music theory and genetic algorithm
Shingchern D You and Po-Sheng Liu · 2016
Earlier work this paper cites.
Mixing rates for the alternating gibbs sampler over restricted boltzmann machines and friends
Christopher Tosh · 2016
Earlier work this paper cites.
Auxiliary deep generative models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
One-shot generalization in deep generative models
Danilo Rezende, Ivo Danihelka, Karol Gregor, Daan Wierstra, et al · 2016
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
Earlier work this paper cites.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
Earlier work this paper cites.
Attribute2image: Conditional image generation from visual attributes
Xinchen Yan, Jimei Yang, Kihyuk Sohn, and Honglak Lee · 2016
Earlier work this paper cites.
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2016
Earlier work this paper cites.
Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Pixelvae: A latent variable model for natural images
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga, Francesco Visin, David Vazquez, and Aaron Courville · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
Aaron Van Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Neural autoregressive distribution estimation
Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, and Hugo Larochelle · 2016
Cited alongside, same era.
Video paragraph captioning using hierarchical recurrent neural networks
Haonan Yu, Jiang Wang, Zhiheng Huang, Yi Yang, and Wei Xu · 2016
Cited alongside, same era.
Tuning recurrent neural networks with re-inforcement learning
Natasha Jaques, Shixiang Gu, Richard E Turner, and Douglas Eck · 2016
Cited alongside, same era.
Sequential neural models with stochastic layers
Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, and Ole Winther · 2016
Imposing higher-level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints
Stefan Lattner, Maarten Grachten, and Gerhard Widmer · 2018
Later among the works it cites.
Defactor: Differentiable edge factorization-based probabilistic graph generation
Rim Assouel, Mohamed Ahmed, Marwin H Segler, Amir Saffari, and Yoshua Bengio · 2018
Later among the works it cites.
It takes (only) two: Adversarial generator-encoder networks
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
Later among the works it cites.
Exploring helmholtz machine and deep belief net in the exponential family perspective
Yifeng Li and Xiaodan Zhu · 2018
Later among the works it cites.
Vae with a vampprior
Jakub Tomczak and Max Welling · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Algorithmic composition of melodies with deep recurrent neural networks
Florian Colombo, Samuel P Muscinelli, Alexander Seeholzer, Johanni Brea, and Wulfram Gerstner · 2016
Cited alongside, same era.
Composing music with grammar argumented neural networks and note-level encoding
Zheng Sun, Jiaqi Liu, Zewang Zhang, Jingwen Chen, Zhao Huo, Ching Hua Lee, and Xiao Zhang · 2016
Cited alongside, same era.
An actor-critic algorithm for sequence prediction
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, and Yoshua Bengio · 2016
Cited alongside, same era.
Review networks for caption generation
Zhilin Yang, Ye Yuan, Yuexin Wu, William W Cohen, and Russ R Salakhutdinov · 2016
Cited alongside, same era.
Image captioning with semantic attention
Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo · 2016
Cited alongside, same era.
Text-based lstm networks for automatic music composition
Keunwoo Choi, George Fazekas, and Mark Sandler · 2016
Cited alongside, same era.
Machine learning-based screening of complex molecules for polymer solar cells
Peter Bjørn Jørgensen, Murat Mesta, Suranjan Shil, Juan Maria García Lastra, Karsten Wedel Jacobsen, Kristian Sommer Thygesen, and Mikkel N Schmidt · 2018
Later among the works it cites.
Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
Later among the works it cites.
A hierarchical latent vector model for learning long-term structure in music
Adam Roberts, Jesse Engel, Colin Raffel, Curtis Hawthorne, and Douglas Eck · 2018
Later among the works it cites.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
Later among the works it cites.
Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt · 2018
Later among the works it cites.
Syntax-directed variational autoencoder for structured data
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song · 2018
Later among the works it cites.
Differentially private data generative models
Qingrong Chen, Chong Xiang, Minhui Xue, Bo Li, Nikita Borisov, Dali Kaarfar, and Haojin Zhu · 2018
Later among the works it cites.
Introvae: Introspective variational autoencoders for photographic image synthesis
Huaibo Huang, zhihang li, Ran He, Zhenan Sun, and Tieniu Tan · 2018
Later among the works it cites.
Conditioning deep generative raw audio models for structured automatic music
Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, and Brian Kulis · 2018
Later among the works it cites.
Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
Later among the works it cites.
Efficient neural audio synthesis
Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron Oord, Sander Dieleman, and Koray Kavukcuoglu · 2018
Later among the works it cites.
Relational recurrent neural networks
Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, and Timothy Lillicrap · 2018
Later among the works it cites.
Deepj: Style-specific music generation
Huanru Henry Mao, Taylor Shin, and Garrison Cottrell · 2018
Later among the works it cites.
Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec · 2018
Later among the works it cites.
Natural tts synthesis by conditioning wavenet on mel spectrogram predictions
Jonathan Shen, Ruoming Pang, Ron J Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, Rj Skerrv-Ryan, et al · 2018
Later among the works it cites.
Generating high fidelity images with subscale pixel networks and multidimensional upscaling
Jacob Menick and Nal Kalchbrenner · 2018
Later among the works it cites.
Generating wikipedia by summarizing long sequences
Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer · 2018
Later among the works it cites.
Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
Later among the works it cites.
Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Ian Simon, Curtis Hawthorne, Andrew M Dai, Matthew D Hoffman, Monica Dinculescu, and Douglas Eck · 2018
Later among the works it cites.
Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani · 2018
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Later among the works it cites.
Synthetic data augmentation using gan for improved liver lesion classification
Maayan Frid-Adar, Eyal Klang, Michal Amitai, Jacob Goldberger, and Hayit Greenspan · 2018
Later among the works it cites.
Chris Donahue, Julian McAuley, and Miller Puckette · 2018
Later among the works it cites.
Mocogan: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
Later among the works it cites.
Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment
Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, and Yi-Hsuan Yang · 2018
Later among the works it cites.
Capsulegan: Generative adversarial capsule network
Ayush Jaiswal, Wael AbdAlmageed, Yue Wu, and Premkumar Natarajan · 2018
Later among the works it cites.
Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
Later among the works it cites.
Netgan: Generating graphs via random walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, and Stephan Günnemann · 2018
Later among the works it cites.
Data synthesis based on generative adversarial networks
Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, Hongkyu Park, and Youngmin Kim · 2018
Later among the works it cites.
Correlated discrete data generation using adversarial training
Shreyas Patel, Ashutosh Kakadiya, Maitrey Mehta, Raj Derasari, Rahul Patel, and Ratnik Gandhi · 2018
Later among the works it cites.
Synthesizing tabular data using generative adversarial networks
Lei Xu and Kalyan Veeramachaneni · 2018
Later among the works it cites.
Improving the improved training of wasserstein gans: A consistency term and its dual effect
Xiang Wei, Boqing Gong, Zixia Liu, Wei Lu, and Liqiang Wang · 2018
Later among the works it cites.
Ambientgan: Generative models from lossy measurements
Ashish Bora, Eric Price, and Alexandros G Dimakis · 2018
Later among the works it cites.
Generative modeling for protein structures
Namrata Anand and Possu Huang · 2018
Later among the works it cites.
A non-parametric generative model for human trajectories
Kun Ouyang, Reza Shokri, David S Rosenblum, and Wenzhuo Yang · 2018
Later among the works it cites.
MGAN: Training generative adversarial nets with multiple generators
Quan Hoang, Tu Dinh Nguyen, Trung Le, and Dinh Phung · 2018
Later among the works it cites.
Relgan: Relational generative adversarial networks for text generation
Weili Nie, Nina Narodytska, and Ankit Patel · 2018
Later among the works it cites.
Dist-gan: An improved gan using distance constraints
Ngoc-Trung Tran, Tuan-Anh Bui, and Ngai-Man Cheung · 2018
Later among the works it cites.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Later among the works it cites.
Learning generative models with sinkhorn divergences
Aude Genevay, Gabriel Peyre, and Marco Cuturi · 2018
Later among the works it cites.
Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar · 2018
Later among the works it cites.
Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
Later among the works it cites.
Synthetic data generation for end-to-end thermal infrared tracking
Lichao Zhang, Abel Gonzalez-Garcia, Joost Van De Weijer, Martin Danelljan, and Fahad Shahbaz Khan · 2018
Later among the works it cites.
Perceptual adversarial networks for image-to-image transformation
Chaoyue Wang, Chang Xu, Chaohui Wang, and Dacheng Tao · 2018
Later among the works it cites.
Text-adaptive generative adversarial networks: manipulating images with natural language
Seonghyeon Nam, Yunji Kim, and Seon Joo Kim · 2018
Later among the works it cites.
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2018
Later among the works it cites.
Generating photo-realistic training data to improve face recognition accuracy
Daniel Sáez Trigueros, Li Meng, and Margaret Hartnett · 2018
Later among the works it cites.
Semi-supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model
Baris Gecer, Binod Bhattarai, Josef Kittler, and Tae-Kyun Kim · 2018
Later among the works it cites.
Xiaojie Guo, Lingfei Wu, and Liang Zhao · 2018
Later among the works it cites.
Maskgan: better text generation via filling in the_
William Fedus, Ian Goodfellow, and Andrew M Dai · 2018
Later among the works it cites.
Medical image synthesis for data augmentation and anonymization using generative adversarial networks
Hoo-Chang Shin, Neil A Tenenholtz, Jameson K Rogers, Christopher G Schwarz, Matthew L Senjem, Jeffrey L Gunter, Katherine P Andriole, and Mark Michalski · 2018
Later among the works it cites.
Generating artificial data for private deep learning
Aleksei Triastcyn and Boi Faltings · 2018
Later among the works it cites.
Improving mmd-gan training with repulsive loss function
Wei Wang, Yuan Sun, and Saman Halgamuge · 2018
Later among the works it cites.
Generative models for simulating mobility trajectories
Vaibhav Kulkarni, Natasa Tagasovska, Thibault Vatter, and Benoit Garbinato · 2018
Later among the works it cites.
Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
Later among the works it cites.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Later among the works it cites.
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Later among the works it cites.
Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
Later among the works it cites.
An introduction to deep reinforcement learning
Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G Bellemare, and Joelle Pineau · 2018
Later among the works it cites.
Toward diverse text generation with inverse reinforcement learning
Zhan Shi, Xinchi Chen, Xipeng Qiu, and Xuanjing Huang · 2018
Later among the works it cites.
Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
Later among the works it cites.
What makes good synthetic training data for learning disparity and optical flow estimation?
Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2018
Later among the works it cites.
Training deep face recognition systems with synthetic data
Adam Kortylewski, Andreas Schneider, Thomas Gerig, Bernhard Egger, Andreas Morel-Forster, and Thomas Vetter · 2018
Later among the works it cites.
Privacy in neural network learning: Threats and countermeasures
Shan Chang and Chao Li · 2018
Later among the works it cites.
How generative adversarial networks and their variants work: An overview
Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, and Sungroh Yoon · 2019
Later among the works it cites.
Towards generating long and coherent text with multi-level latent variable models
Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen, Xin Wang, Jianfeng Gao, and Lawrence Carin · 2019
Later among the works it cites.
Topic-guided variational autoencoders for text generation
Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, and Lawrence Carin · 2019
Later among the works it cites.
A two-step graph convolutional decoder for molecule generation
Xavier Bresson and Thomas Laurent · 2019
Later among the works it cites.
Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K Duvenaud, Raquel Urtasun, and Richard Zemel · 2019
Later among the works it cites.
Graph generation by sequential edge prediction
Davide Bacciu, Alessio Micheli, and Marco Podda · 2019
Later among the works it cites.
Molecularrnn: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev · 2019
Later among the works it cites.
Deep generative graph distribution learning for synthetic power grids
Mahdi Khodayar, Jianhui Wang, and Zhaoyu Wang · 2019
Later among the works it cites.
End-to-end image super-resolution via deep and shallow convolutional networks
Yifan Wang, Lijun Wang, Hongyu Wang, and Peihua Li · 2019
Later among the works it cites.
Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
Later among the works it cites.
Videobert: A joint model for video and language representation learning
Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid · 2019
Later among the works it cites.
Auto-regressive graph generation modeling with improved evaluation methods
Chia-Cheng Liu, Harris Chan, Kevin Luk, and AI Borealis · 2019
Later among the works it cites.
Bayesian modelling and monte carlo inference for GAN
Hao He, Hao Wang, Guang-He Lee, and Yonglong Tian · 2019
Later among the works it cites.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Later among the works it cites.
Adversarial sub-sequence for text generation
Xingyuan Chen, Yanzhe Li, Peng Jin, Jiuhua Zhang, Xinyu Dai, Jiajun Chen, and Gang Song · 2019
Later among the works it cites.
Adversarial pixel-level generation of semantic images
Emanuele Ghelfi, Paolo Galeone, Michele De Simoni, and Federico Di Mattia · 2019
Later among the works it cites.
Synthesizing electronic health records using improved generative adversarial networks
Mrinal Kanti Baowaly, Chia-Ching Lin, Chao-Lin Liu, and Kuan-Ta Chen · 2019
Later among the works it cites.
Generating synthetic medical images by using gan to improve cnn performance in skin cancer classification
Pooyan Sedigh, Rasoul Sadeghian, and Mehdi Tale Masouleh · 2019
Later among the works it cites.
Autogan: Neural architecture search for generative adversarial networks
Xinyu Gong, Shiyu Chang, Yifan Jiang, and Zhangyang Wang · 2019
Later among the works it cites.
Generating high-fidelity, synthetic time series datasets with doppelganger
Zinan Lin, Alankar Jain, Chen Wang, Giulia C. Fanti, and Vyas Sekar · 2019
Later among the works it cites.
Time-series generative adversarial networks
Jinsung Yoon, Daniel Jarrett, and Mihaela van der Schaar · 2019
Later among the works it cites.
Self-attention generative adversarial networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena · 2019
Later among the works it cites.
Medical (ct) image generation with style
Arjun Krishna and Klaus Mueller · 2019
Later among the works it cites.
Adversarial generation of handwritten text images conditioned on sequences
Eloi Alonso, Bastien Moysset, and Ronaldo Messina · 2019
Later among the works it cites.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Later among the works it cites.
High-fidelity image generation with fewer labels
Mario Lučić, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, and Sylvain Gelly · 2019
Later among the works it cites.
Gansynth: Adversarial neural audio synthesis
Jesse Engel, Kumar Krishna Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, and Adam Roberts · 2019
Later among the works it cites.
Labeled graph generative adversarial networks
Shuangfei Fan and Bert Huang · 2019
Later among the works it cites.
Dp-cgan: Differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
Later among the works it cites.
Misc-gan: A multi-scale generative model for graphs
Dawei Zhou, Lecheng Zheng, Jiejun Xu, and Jingrui He · 2019
Later among the works it cites.
Coco-gan: Generation by parts via conditional coordinating
Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, and Hwann-Tzong Chen · 2019
Later among the works it cites.
Large scale adversarial representation learning
Jeff Donahue and Karen Simonyan · 2019
Later among the works it cites.
Copulas as high-dimensional generative models: Vine copula autoencoders
Natasa Tagasovska, Damien Ackerer, and Thibault Vatter · 2019
Later among the works it cites.
Graphnvp: An invertible flow model for generating molecular graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe · 2019
Later among the works it cites.
Paintbot: A reinforcement learning approach for natural media painting
Biao Jia, Chen Fang, Jonathan Brandt, Byungmoon Kim, and Dinesh Manocha · 2019
Later among the works it cites.
Polyphonic music composition with lstm neural networks and reinforcement learning
Harish Kumar and Balaraman Ravindran · 2019
Later among the works it cites.
Meta-sim: Learning to generate synthetic datasets
Amlan Kar, Aayush Prakash, Ming-Yu Liu, Eric Cameracci, Justin Yuan, Matt Rusiniak, David Acuna, Antonio Torralba, and Sanja Fidler · 2019
Later among the works it cites.
Generative adversarial networks in computer vision: A survey and taxonomy
Zhengwei Wang, Qi She, and Tomas E Ward · 2019
Later among the works it cites.
Generative adversarial network in medical imaging: A review
Xin Yi, Ekta Walia, and Paul Babyn · 2019
Later among the works it cites.
Mixing real and synthetic data to enhance neural network training–a review of current approaches
Viktor Seib, Benjamin Lange, and Stefan Wirtz · 2020
Later among the works it cites.
A systematic survey on deep generative models for graph generation
Xiaojie Guo and Liang Zhao · 2020
Later among the works it cites.
A survey of image synthesis methods for visual machine learning
Apostolia Tsirikoglou, Gabriel Eilertsen, and Jonas Unger · 2020
Later among the works it cites.
The survey: Text generation models in deep learning
Touseef Iqbal and Shaima Qureshi · 2020
Later among the works it cites.
A comprehensive survey and analysis of generative models in machine learning
GM Harshvardhan, Mahendra Kumar Gourisaria, Manjusha Pandey, and Siddharth Swarup Rautaray · 2020
Later among the works it cites.
Synthetic data generation through statistical explosion: Improving classification accuracy of coronary artery disease using ppg
Sakyajit Bhattacharya, Oishee Mazumder, Dibyendu Roy, Aniruddha Sinha, and Avik Ghose · 2020
Later among the works it cites.
Generating high-fidelity synthetic patient data for assessing machine learning healthcare software
Allan Tucker, Zhenchen Wang, Ylenia Rotalinti, and Puja Myles · 2020
Later among the works it cites.
Synthesizing tabular data using conditional GAN
Lei Xu et al · 2020
Later among the works it cites.
Nevae: A deep generative model for molecular graphs
Bidisha Samanta, Abir De, Gourhari Jana, Vicenç Gómez, Pratim Kumar Chattaraj, Niloy Ganguly, and Manuel Gomez-Rodriguez · 2020
Later among the works it cites.
Interpretable deep graph generation with node-edge co-disentanglement
Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, and Yanfang Ye · 2020
Later among the works it cites.
Handling incomplete heterogeneous data using vaes
Alfredo Nazabal, Pablo M Olmos, Zoubin Ghahramani, and Isabel Valera · 2020
Later among the works it cites.
Graph deconvolutional generation
Daniel Flam-Shepherd, Tony Wu, and Alan Aspuru-Guzik · 2020
Later among the works it cites.
A deep generative model for fragment-based molecule generation
Marco Podda, Davide Bacciu, and Alessio Micheli · 2020
Later among the works it cites.
Variational sparse coding
Francesco Tonolini, Bjørn Sand Jensen, and Roderick Murray-Smith · 2020
Later among the works it cites.
This time with feeling: Learning expressive musical performance
Sageev Oore, Ian Simon, Sander Dieleman, Douglas Eck, and Karen Simonyan · 2020
Later among the works it cites.
A novel approach to create synthetic biomedical signals using birnn
Andres Hernandez-Matamoros, Hamido Fujita, and Hector Perez-Meana · 2020
Later among the works it cites.
Graphgen: a scalable approach to domain-agnostic labeled graph generation
Nikhil Goyal, Harsh Vardhan Jain, and Sayan Ranu · 2020
Later among the works it cites.
Generative adversarial networks based on collaborative learning and attention mechanism for hyperspectral image classification
Jie Feng, Xueliang Feng, Jiantong Chen, Xianghai Cao, Xiangrong Zhang, Licheng Jiao, and Tao Yu · 2020
Later among the works it cites.
Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Later among the works it cites.
Generation and evaluation of privacy preserving synthetic health data
Andrew Yale, Saloni Dash, Ritik Dutta, Isabelle Guyon, Adrien Pavao, and Kristin P Bennett · 2020
Later among the works it cites.
Synthetic brain image generation for adhd prediction based on progressive growing generative adversarial network
Saadia Binte Alam, Moazzem Hossain, and Syoji Kobashi · 2020
Later among the works it cites.
Gan-based synthetic brain pet image generation
Jyoti Islam and Yanqing Zhang · 2020
Later among the works it cites.
Synsiggan: Generative adversarial networks for synthetic biomedical signal generation
Debapriya Hazra and Yung-Cheol Byun · 2020
Later among the works it cites.
Adversarialnas: Adversarial neural architecture search for gans
Chen Gao, Yunpeng Chen, Si Liu, Zhenxiong Tan, and Shuicheng Yan · 2020
Later among the works it cites.
Cot-gan: Generating sequential data via causal optimal transport
Tianlin Xu, Li Kevin Wenliang, Michael Munn, and Beatrice Acciaio · 2020
Later among the works it cites.
Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
Later among the works it cites.
Medgan: Medical image translation using gans
Karim Armanious, Chenming Jiang, Marc Fischer, Thomas Küstner, Tobias Hepp, Konstantin Nikolaou, Sergios Gatidis, and Bin Yang · 2020
Later among the works it cites.
Mol-cyclegan: a generative model for molecular optimization
Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Tomasz Danel, and Michał Warchoł · 2020
Later among the works it cites.
Smooth-gan: towards sharp and smooth synthetic ehr data generation
Sina Rashidian, Fusheng Wang, Richard Moffitt, Victor Garcia, Anurag Dutt, Wei Chang, Vishwam Pandya, Janos Hajagos, Mary Saltz, and Joel Saltz · 2020
Later among the works it cites.
Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 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.
Synthetic data for deep learning
Sergey I Nikolenko et al · 2021
Later among the works it cites.
Deep generative models for synthetic data
Peter Eigenschink, Stefan Vamosi, Ralf Vamosi, Chang Sun, Thomas Reutterer, and Klaudius Kalcher · 2021
Later among the works it cites.
Bayesboost: Identifying and handling bias using synthetic data generators
Barbara Draghi, Zhenchen Wang, Puja Myles, and Allan Tucker · 2021
Later among the works it cites.
Time series data augmentation using time-warped auto-encoders
Shain Shahid Chowdhury, Soukaïna Filali Boubrahimi, and Shah Muhammad Hamdi · 2021
Later among the works it cites.
Synthetic data generation using dcgan for improved traffic sign recognition
Christine Dewi, Rung-Ching Chen, Yan-Ting Liu, and Shao-Kuo Tai · 2021
Later among the works it cites.
Synthetic and private smart health care data generation using gans
Sana Imtiaz, Muhammad Arsalan, Vladimir Vlassov, and Ramin Sadre · 2021
Later among the works it cites.
Deepfake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine
Vajira Thambawita, Jonas L Isaksen, Steven A Hicks, Jonas Ghouse, Gustav Ahlberg, Allan Linneberg, Niels Grarup, Christina Ellervik, Morten Salling Olesen, Torben Hansen, et al · 2021
Later among the works it cites.
Wg2an: Synthetic wound image generation using generative adversarial network
Salih Sarp, Murat Kuzlu, Emmanuel Wilson, and Ozgur Guler · 2021
Later among the works it cites.
Quantum machine learning architecture for covid-19 classification based on synthetic data generation using conditional adversarial neural network
Javaria Amin, Muhammad Sharif, Nadia Gul, Seifedine Kadry, and Chinmay Chakraborty · 2021
Later among the works it cites.
Transgan: Two pure transformers can make one strong gan, and that can scale up
Yifan Jiang, Shiyu Chang, and Zhangyang Wang · 2021
Later among the works it cites.
A semi-supervised autoencoder framework for joint generation and classification of breathing
Oscar Pastor-Serrano, Danny Lathouwers, and Zoltán Perkó · 2021
Later among the works it cites.
Generative time-series modeling with fourier flows
Ahmed Alaa, Alex James Chan, and Mihaela van der Schaar · 2021
Later among the works it cites.
Image synthesis for data augmentation in medical ct using deep reinforcement learning
Arjun Krishna, Kedar Bartake, Chuang Niu, Ge Wang, Youfang Lai, Xun Jia, and Klaus Mueller · 2021
Later among the works it cites.
Time-series generation by contrastive imitation
Daniel Jarrett, Ioana Bica, and Mihaela van der Schaar · 2021
Later among the works it cites.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Later among the works it cites.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Later among the works it cites.
Synthetic data generation for optical flow evaluation in the neurosurgical domain
Markus Philipp, Neal Bacher, Jonas Nienhaus, Lars Hauptmann, Laura Lang, Anna Alperovich, Marielena Gutt-Will, Andrea Mathis, Stefan Saur, Andreas Raabe, et al · 2021
Later among the works it cites.
Synthetic data generation for steel defect detection and classification using deep learning
Aleksei Boikov, Vladimir Payor, Roman Savelev, and Alexandr Kolesnikov · 2021
Later among the works it cites.
Fake it till you make it: Guidelines for effective synthetic data generation
Fida K Dankar and Mahmoud Ibrahim · 2021
Later among the works it cites.
A survey of synthetic data generation for machine learning
Mohammad Abufadda and Khalid Mansour · 2021
Later among the works it cites.
Is ai eating software? an analysis of ai/ml research trends using scientific pre-prints
Konstantinos Stathoulopoulos, Joel Klinger, and Juan Mateos-Garcia · 2022
Later among the works it cites.
A multi-dimensional evaluation of synthetic data generators
Fida K Dankar, Mahmoud K Ibrahim, and Leila Ismail · 2022
Later among the works it cites.
Pros and cons of gan evaluation measures: New developments
Ali Borji · 2022
Later among the works it cites.
Deephear – composing and harmonizing music with neural networks
Felix Sun · 2022
Later among the works it cites.
Generating long-term structure in songs and stories
Elliot Waite et al · 2022
Later among the works it cites.
A creative tool for the musician combining lstm and markov chains in max/msp
Nicola Privato, Omar Rampado, and Alberto Novello · 2022
Later among the works it cites.
Tts-gan: A transformer-based time-series generative adversarial network
Xiaomin Li, Vangelis Metsis, Huangyingrui Wang, and Anne Hee Hiong Ngu · 2022
Later among the works it cites.
Scenario generation for cooling, heating, and power loads using generative moment matching networks
Wenlong Liao, Yusen Wang, Yuelong Wang, Kody Powell, Qi Liu, and Zhe Yang · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Later among the works it cites.
https://www.blender.org/
Blender - free and open 3d creation software · 2022
Later among the works it cites.
https://www.povray.org/
The persistence of vision raytracer · 2022
Later among the works it cites.
https://vdrift.net/
Vdrift - open-source driving simulation · 2022
Later among the works it cites.
https://unity.com/
Unity real-time development platform · 2022
Later among the works it cites.