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

[Network In Network]

• Network In Network [arXiv]
• Min Lin, Qiang Chen, Shuicheng Yan
• 2013-12-13

[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

[Human-level control through deep reinforcement learning]

• Human-level control through deep reinforcement learning [nature]
• Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg & Demis Hassabis
• 2015-02-25

[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

[Deep Reinforcement Learning with Double Q-learning]

• Deep Reinforcement Learning with Double Q-learning [arXiv]
• Hado van Hasselt, Arthur Guez, David Silver
• 2015-09-22

[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

[Image-to-Image Translation with Conditional Adversarial Networks]

• Image-to-Image Translation with Conditional Adversarial Networks [arXiv]
• Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
• 2016-11-21

[Wasserstein GAN]

• Wasserstein GAN [arXiv]
• Martin Arjovsky, Soumith Chintala, Léon Bottou
• 2017-01-26

[Density estimation using Real NVP]

• Density estimation using Real NVP [arXiv]
• Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio
• 2017-02-27

[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

[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks]

• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks [arXiv]
• Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros
• 2017-03-30

[Improved Training of Wasserstein GANs]

• Improved Training of Wasserstein GANs [arXiv]
• Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville
• 2017-03-31

[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

[Masked Autoregressive Flow for Density Estimation]

• Masked Autoregressive Flow for Density Estimation [arXiv]
• George Papamakarios, Theo Pavlakou, Iain Murray
• 2017-06-14

[Proximal Policy Optimization Algorithms]

• Proximal Policy Optimization Algorithms [arXiv]
• John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
• 2017-08-28

[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

[Denoising Diffusion Implicit Models]

• Denoising Diffusion Implicit Models [arXiv]
• Jiaming Song, Chenlin Meng, Stefano Ermon
• 2020-10-06

[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

[Stable Diffusion v1-5]

• High-Resolution Image Synthesis with Latent Diffusion Models [arXiv]
• Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer
• 2021-12-20

[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

[Instant Neural Graphics Primitives with a Multiresolution Hash Encoding]

• Instant Neural Graphics Primitives with a Multiresolution Hash Encoding [arXiv]
• Thomas Müller, Alex Evans, Christoph Schied, Alexander Keller
• 2022-01-16

[Pseudo Numerical Methods for Diffusion Models on Manifolds]

• Pseudo Numerical Methods for Diffusion Models on Manifolds [arXiv]
• Luping Liu, Yi Ren, Zhijie Lin, Zhou Zhao
• 2022-02-30

[DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps]

• DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps [arXiv]
• Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu
• 2022-06-02

[DreamBooth]

• DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation [arXiv]
• Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, Kfir Aberman
• 2022-08-25

[Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow]

• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow [arXiv]
• Xingchao Liu, Chengyue Gong, Qiang Liu
• 2022-09-07

[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,