Adversarial Learning 에 대한 overview 를 해주시는 Ian Goodfellow 의 ICLR 2019 키노트입니다.
이 글을 읽어서 무엇을 배울 수 있는지 알려주세요! 🤔
Adversarial Learning 의 발전사를 간단하게 다룹니다.
Adversarial Learning 의 최근 연구 결과들을 overview 해볼 수 있습니다.
다양한 도메인에서의 Adversarial Learning 연구 결과들에 대한 overview
같이 읽어보면 좋을 만한 글이나 이슈가 있을까요?
Until about 2013, most researchers studying machine learning for artificial intelligence all worked on a common goal: get machine learning to work for AI-scale tasks. Now that supervised learning works, there is a Cambrian explosion of new research directions: making machine learning secure, making machine learning private, getting machine learning to work for new tasks, reducing the dependence on large amounts of labeled data, and so on. In this talk I survey how adversarial techniques in machine learning are involved in several of these new research frontiers.
어떤 내용의 레퍼런스인가요? 👋
Adversarial Learning 에 대한 overview 를 해주시는 Ian Goodfellow 의 ICLR 2019 키노트입니다.
이 글을 읽어서 무엇을 배울 수 있는지 알려주세요! 🤔
같이 읽어보면 좋을 만한 글이나 이슈가 있을까요?
Until about 2013, most researchers studying machine learning for artificial intelligence all worked on a common goal: get machine learning to work for AI-scale tasks. Now that supervised learning works, there is a Cambrian explosion of new research directions: making machine learning secure, making machine learning private, getting machine learning to work for new tasks, reducing the dependence on large amounts of labeled data, and so on. In this talk I survey how adversarial techniques in machine learning are involved in several of these new research frontiers.
레퍼런스의 URL을 알려주세요! 🔗
https://www.youtube.com/watch?v=sucqskXRkss