The Optimal Noise in Noise-Contrastive Learning Is Not What You Think

Matplotlib: A 2d graphics environment

John D Hunter · 2007

Earlier work this paper cites.

A family of computationally efficient and simple estimators for unnormalized statistical models

Miika Pihlaja, Michael Gutmann, and Aapo Hyvärinen · 2010

Earlier work this paper cites.

Bregman divergence as general framework to estimate unnormalized statistical models

Michael Gutmann and Jun ichiro Hirayama · 2011

Earlier work this paper cites.

Scikit-learn: Machine Learning in Python

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Matthieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay · 2011

Earlier work this paper cites.

Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics

Michael Gutmann and Aapo Hyvärinen · 2012

Earlier work this paper cites.

A fast and simple algorithm for training neural probabilistic language models

Andriy Mnih and Yee Whye Teh · 2012

Earlier work this paper cites.

Distributed representations of words and phrases and their compositionality

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013

Earlier work this paper cites.

Generative adversarial nets

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014

Cited alongside, same era.

Linking losses for density ratio and class-probability estimation

Aditya Menon and Cheng Soon Ong · 2016

Cited alongside, same era.

Learning in implicit generative models

Original

Shakir Mohamed and Balaji Lakshminarayanan · 2016

Cited alongside, same era.

Noise contrastive estimation and negative sampling for conditional models: Consistency and statistical efficiency

Zhuang Ma and Michael Collins · 2018

Cited alongside, same era.

Noise contrastive estimation: Asymptotic properties, formal comparison with MC-MLE

Lionel Riou-Durand and Nicolas Chopin · 2018

Cited alongside, same era.

Prescribed generative adversarial networks

Original

Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, and Michalis K. Titsias · 2019

Later among the works it cites.

Pytorch: An imperative style, high-performance deep learning library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019

Later among the works it cites.

A simple framework for contrastive learning of visual representations

Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020

Later among the works it cites.

Flow contrastive estimation of energy-based models

Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, and Ying Nian Wu · 2020

Later among the works it cites.

Array programming with NumPy

Charles R. Harris, K. Jarrod Millman, St’efan 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 Fern’andez del R’ıo, Mark Wiebe, Pearu Peterson, Pierre G’erard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020