Papers in 100 Lines of Code

This repository, Papers in 100 Lines of Code, offers straightforward implementations of various significant research papers in machine learning and artificial intelligence, encapsulating complex concepts into concise code examples, each limited to just 100 lines. This allows users to engage with cutting-edge methodologies while maintaining clarity.

Implemented Papers

• Maxout Networks
  Ian J. Goodfellow et al.
  Released on: 2013-02-18

• Network In Network
  Min Lin et al.
  Released on: 2013-12-13

• Playing Atari with Deep Reinforcement Learning
  Volodymyr Mnih et al.
  Released on: 2013-12-19

• Auto-Encoding Variational Bayes
  Diederik P Kingma, Max Welling
  Released on: 2013-12-20

• Generative Adversarial Networks
  Ian J. Goodfellow et al.
  Released on: 2014-06-10

• Conditional Generative Adversarial Nets
  Mehdi Mirza, Simon Osindero
  Released on: 2014-11-06

• Adam: A Method for Stochastic Optimization
  Diederik P. Kingma, Jimmy Ba
  Released on: 2014-12-22

• NICE: Non-linear Independent Components Estimation
  Laurent Dinh et al.
  Released on: 2014-10-30

• Human-level control through deep reinforcement learning
  Volodymyr Mnih et al.
  Released on: 2015-02-25

• Deep Unsupervised Learning using Nonequilibrium Thermodynamics
  Jascha Sohl-Dickstein et al.
  Released on: 2015-03-12

For a complete list of implemented papers, including seminal works such as Variational Inference with Normalizing Flows and Adversarial Feature Learning, please view the repository directly.

This project serves as a useful resource for developers, researchers, and students in the field looking to learn and apply machine learning techniques through practical examples.