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
[Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision]
- Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision [arXiv]
- Michael Niemeyer, Lars Mescheder, Michael Oechsle, Andreas Geiger
2019-12-16
[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
[pixelNeRF: Neural Radiance Fields from One or Few Images]
- pixelNeRF: Neural Radiance Fields from One or Few Images [arXiv]
- Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa
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
[Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering]
- Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering [arXiv]
- Vincent Sitzmann, Semon Rezchikov, William T. Freeman, Joshua B. Tenenbaum, Fredo Durand
2021-06-04
[Gromov-Wasserstein Distances between Gaussian Dist