Papers in 100 Lines of Code

Implementation of papers in 100 lines of code.

Implemented papers

[Maxout Networks]

• Maxout Networks [arXiv]
• Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio
• 2013-02-18

[Auto-Encoding Variational Bayes]

• Auto-Encoding Variational Bayes [arXiv]
• Diederik P Kingma, Max Welling
• 2013-12-20

[Generative Adversarial Networks]

• Generative Adversarial Networks [arXiv]
• Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
• 2014-06-10

[Conditional Generative Adversarial Nets]

• Conditional Generative Adversarial Nets [arXiv]
• Mehdi Mirza, Simon Osindero
• 2014-11-06

[NICE: Non-linear Independent Components Estimation]

• NICE: Non-linear Independent Components Estimation [arXiv]
• Laurent Dinh, David Krueger, Yoshua Bengio
• 2014-10-30

[Variational Inference with Normalizing Flows]

• Variational Inference with Normalizing Flows [arXiv]
• Danilo Jimenez Rezende, Shakir Mohamed
• 2015-05-21

[Improved Techniques for Training GANs]

• Improved Techniques for Training GANs [arXiv]
• Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen
• 2016-06-10

[Least Squares Generative Adversarial Networks]

• Least Squares Generative Adversarial Networks [arXiv]
• Xudong Mao, Qing Li, Haoran Xie, Raymond Y.K. Lau, Zhen Wang, Stephen Paul Smolley
• 2016-11-13

[Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks]

• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks [arXiv]
• Chelsea Finn, Pieter Abbeel, Sergey Levine
• 2017-03-09

[On First-Order Meta-Learning Algorithms]

• On First-Order Meta-Learning Algorithms [arXiv]
• Alex Nichol, Joshua Achiam, John Schulman
• 2018-03-08

[Sequential Neural Likelihood]

• Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows [arXiv]
• George Papamakarios, David C. Sterratt, Iain Murray
• 2018-05-18

[Optimizing Millions of Hyperparameters by Implicit Differentiation]

• Optimizing Millions of Hyperparameters by Implicit Differentiation [PMLR]
• Jonathan Lorraine, Paul Vicol, David Duvenaud
• 2019-10-06

[Implicit Neural Representations with Periodic Activation Functions]

• Implicit Neural Representations with Periodic Activation Functions [arXiv]
• Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein
• 2020-06-17

[Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains]

• Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains [arXiv]
• Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng
• 2020-06-18

[Likelihood-free MCMC with Amortized Approximate Ratio Estimators]

• Likelihood-free MCMC with Amortized Approximate Ratio Estimators [PMLR]
• Joeri Hermans, Volodimir Begy, Gilles Louppe
• 2020-06-26

[NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis]

• NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis [arXiv]
• Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng
• 2020-08-03

[Multiplicative Filter Networks]

• Multiplicative Filter Networks [OpenReview]
• Rizal Fathony, Anit Kumar Sahu, Devin Willmott, J Zico Kolter
• 2020-09-28

[Gromov-Wasserstein Distances between Gaussian Distributions]

• Gromov-Wasserstein Distances between Gaussian Distributions [arXiv]
• Antoine Salmona, Julie Delon, Agnès Desolneux
• 2021-08-16