【导读】人工智能领域的国际顶级会议 AAAI 2019 于 1 月 27 日至 2 月 1 日在美国夏威夷举行。 AAAI会议日程之紧凑，会议内容之丰富，令人目不暇接。如果你回家过年，或者因为奔波在各个会场而漏听了感兴趣的报告，这里有一份来自布朗大学David Abel博士的 77页的AAAI 2019的会议笔记，了解一下。
今天，美国布朗大学的一位同学 David Abel，公开分享了他在这次大会上整理的长达77页的会议笔记。这份笔记内容详实，包含了不少来自现场拍摄的PPT图片，还根据参会日期和会场做了详细的目录。 今年重点关注的内容包括机器学习、视觉和NLP、博弈论、启发式搜索和认知系统。
Weighted model counting has emerged as a prevalent approach for probabilistic inference. In this paper, we are interested in weighted DNF counting, or briefly, weighted #DNF, which admits a fully polynomial randomized approximation scheme, as shown by Karp and Luby. To this date, the best algorithm for approximating #DNF is due to Karp, Luby and Madras. The drawback of this algorithm is that it runs in quadratic time and hence is not suitable for fast online reasoning. To overcome this, we propose a novel approach that combines approximate model counting with deep learning. We conduct detailed experiments to validate our approach, and show that our model learns and generalizes from #DNF instances with a very high accuracy.
People naturally bring their prior beliefs to bear on how they interpret the new information, yet few formal models exist for accounting for the influence of users' prior beliefs in interactions with data presentations like visualizations. We demonstrate a Bayesian cognitive model for understanding how people interpret visualizations in light of prior beliefs and show how this model provides a guide for improving visualization evaluation. In a first study, we show how applying a Bayesian cognition model to a simple visualization scenario indicates that people's judgments are consistent with a hypothesis that they are doing approximate Bayesian inference. In a second study, we evaluate how sensitive our observations of Bayesian behavior are to different techniques for eliciting people subjective distributions, and to different datasets. We find that people don't behave consistently with Bayesian predictions for large sample size datasets, and this difference cannot be explained by elicitation technique. In a final study, we show how normative Bayesian inference can be used as an evaluation framework for visualizations, including of uncertainty.
Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance, and a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many game environments. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at https://pathak22.github.io/large-scale-curiosity/