2019 in Review: 10 AI Papers That Made an Impact

The volume of peer-reviewed AI research papers has grown by more than 300 percent over the past three decades (Stanford AI Index 2019), and the top AI conferences in 2019 saw a deluge of paper. CVPR submissions spiked to 5,165, a 56 percent increase over 2018; ICLR received 1,591 main conference paper submissions, up 60 percent over last year; ACL reported a record-breaking 2,906 submissions, almost doubling last year's 1,544; and ICCV 2019 received 4,303 submissions, more than twice the 2017 total. As part of our year-end series, Synced spotlights 10 artificial intelligence papers that garnered extraordinary attention and accolades in 2019. Abstract: Finite-horizon lookahead policies are abundantly used in Reinforcement Learning and demonstrate impressive empirical success. Usually, the lookahead policies are implemented with specific planning methods such as Monte Carlo Tree Search (e.g. in AlphaZero).