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

[Playing Atari with Deep Reinforcement Learning]

• Playing Atari with Deep Reinforcement Learning [arXiv]
• Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
• 2013-12-19

[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

[Adam: A Method for Stochastic Optimization]

• Adam: A Method for Stochastic Optimization [arXiv]
• Diederik P. Kingma, Jimmy Ba
• 2014-12-22

[NICE: Non-linear Independent Components Estimation]

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

[Deep Unsupervised Learning using Nonequilibrium Thermodynamics]

• Deep Unsupervised Learning using Nonequilibrium Thermodynamics [arXiv]
• Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli
• 2015-03-12

[Variational Inference with Normalizing Flows]

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

[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks]

• Convolutional Generative Adversarial Networks [arXiv]
• Alec Radford, Luke Metz, Soumith Chintala
• 2015-11-19

[Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)]

• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) [arXiv]
• Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
• 2015-11-23

[Adversarially Learned Inference]

• Adversarially Learned Inference [arXiv]
• Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, Aaron Courville
• 2016-06-02

[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

[Gaussian Error Linear Units (GELUs)]

• Gaussian Error Linear Units (GELUs) [arXiv]
• Dan Hendrycks, Kevin Gimpel
• 2016-06-27

[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

[Adversarial Feature Learning]

• Adversarial Feature Learning [arXiv]
• Jeff Donahue, Philipp Krähenbühl, Trevor Darrell
• 2017-04-03

[Self-Normalizing Neural Networks]

• Self-Normalizing Neural Networks [arXiv]
• Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
• 2017-06-08

[Deep Image Prior]

• Deep Image Prior [arXiv]
• Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
• 2017-11-29

[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

[On the Variance of the Adaptive Learning Rate and Beyond]

• On the Variance of the Adaptive Learning Rate and Beyond [arXiv]
• Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, Jiawei Han
• 2019-08-08

[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

[Denoising Diffusion Probabilistic Models]

• Denoising Diffusion Probabilistic Models [arXiv]
• Jonathan Ho, Ajay Jain, Pieter Abbeel
• 2020-06-19

[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

[Learned Initializations for Optimizing Coordinate-Based Neural Representations]

• Learned Initializations for Optimizing Coordinate-Based Neural Representations [arXiv]
• Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, Ren Ng
• 2020-12-03

[FastNeRF: High-Fidelity Neural Rendering at 200FPS]

• FastNeRF: High-Fidelity Neural Rendering at 200FPS [arXiv]
• Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, Julien Valentin
• 2021-03-18

[KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs]

• KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs [arXiv]
• Christian Reiser, Songyou Peng, Yiyi Liao, Andreas Geiger
• 2021-03-25

[PlenOctrees for Real-time Rendering of Neural Radiance Fields]

• PlenOctrees for Real-time Rendering of Neural Radiance Fields [arXiv]
• Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, Angjoo Kanazawa
• 2021-03-25

[NeRF--: Neural Radiance Fields Without Known Camera Parameters]

• NeRF--: Neural Radiance Fields Without Known Camera Parameters [arXiv]
• Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, Victor Adrian Prisacariu
• 2021-02-14

[Gromov-Wasserstein Distances between Gaussian Distributions]

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

[Plenoxels: Radiance Fields without Neural Networks]

• Plenoxels: Radiance Fields without Neural Networks [arXiv]
• Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa
• 2021-12-09

[InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering]

• InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering [arXiv]
• Mijeong Kim, Seonguk Seo, Bohyung Han
• 2021-12-31

[K-Planes: Explicit Radiance Fields in Space, Time, and Appearance]

• K-Planes: Explicit Radiance Fields in Space, Time, and Appearance [arXiv]
• Sara Fridovich-Keil, Giacomo Meanti, Frederik Warburg, Benjamin Recht, Angjoo Kanazawa
• 2023-01-24

[FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization]

• FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization [arXiv]
• Jiawei Yang, Marco Pavone, Yue Wang
• 2023-03-13