2019년 AI & 머신러닝 분야 주요 논문 Top 10

올해 AI 부분에서 가장 의미있는 10개의 논문을 추리고, 각 논문의 요약, 주요 성취, AI 커뮤니티들의 반응 및 수상내역, 관련한 미래 추가 연구영역, 적용가능한 비즈니스 분야, 구현 코드 위치 등을 정리.

1. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
2. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
3. Meta-Learning Update Rules for Unsupervised Representation Learning
4. On the Variance of the Adaptive Learning Rate and Beyond
5. XLNet: Generalized Autoregressive Pretraining for Language Understanding
6. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
7. Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
8. A Theory of Fermat Paths for Non-Line-of-Sight Shape Reconstruction
9. Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
10. Learning Existing Social Conventions via Observationally Augmented Self-Play