Machine learning is a rapidly evolving field with numerous research papers being published every day. Here are some key papers that have made significant contributions to the field.

Key Papers

  • "Playing Atari with Deep Reinforcement Learning" by Volodymyr Mnih et al. (2013) This paper introduces the concept of Deep Q-Networks (DQN) and demonstrates its effectiveness in playing Atari games.

  • "Generative Adversarial Nets" by Ian Goodfellow et al. (2014) This paper introduces Generative Adversarial Networks (GANs) and their applications in generating realistic images.

  • "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" by Jacob Devlin et al. (2018) This paper introduces BERT, a pre-trained language representation model that has been widely used in natural language processing tasks.

Further Reading

For more in-depth understanding of machine learning, you can explore the following resources:

Machine Learning