本文介绍了学习迭代查询细化的元策略的设计代理的首先成功步骤。我们的方法使用机器读取来指导从聚合搜索结果中选择细化项。然后，使用简单但有效的搜索操作员能够赋予代理，以对查询和搜索结果发挥细粒度和透明控制。我们开发一种新颖的方式来发电综合搜索会话，它通过（自我）监督学习来利用基于变压器的语言模型的力量。我们还提出了一种强化学习代理，具有动态约束的动作，从划痕中了解互动搜索策略。我们使用传统的基于术语的BM25排名函数获得与最近神经方法相当的检索和回答质量性能。我们对搜索政策进行了深入的分析。

越来越需要在各种新的硬件平台上为不同任务部署机器学习。这样的部署场景需要应对多个挑战，包括确定可以实现合适的预测准确性（体系结构搜索）的模型体系结构，并找到有效的模型实施，以满足基础硬件特定的系统约束，例如延迟（系统优化搜索）。现有作品将架构搜索和系统优化搜索视为单独的问题，并将其顺序解决。在本文中，我们建议共同解决这些问题，并引入一种简单但有效的基线方法，称为Sonar，该方法交织了这两个搜索问题。 Sonar的目标是通过将早期停止应用于两个搜索过程来有效地优化预测准确性和推理潜伏期。我们对多个不同硬件后端的实验表明，Sonar识别出几乎最佳体系结构的速度比蛮力方法快30倍。

除了近年来数据收集和分析技术的快速开发外，还越来越强调需要解决与此类数据使用相关的信息泄漏。为此，隐私文献中的许多工作都致力于保护个人用户和数据贡献者。但是，某些情况需要不同的数据机密性概念，涉及数据集记录的全局属性。这样的信息保护概念尤其适用于业务和组织数据，在这些数据中，全球财产可能反映商业秘密或人口统计数据，如果不当行为可能是有害的。最新关于财产推断攻击的工作还显示了数据分析算法如何容易泄漏数据的这些全局性能，从而强调了开发可以保护此类信息的机制的重要性。在这项工作中，我们演示了如何应用分发隐私框架来形式化保护数据集的全球属性的问题。鉴于此框架，我们研究了一些提供数据机密性概念的机制及其权衡。我们分析了这些机制在各种数据假设下提供的理论保护保证，然后对几个数据分析任务进行实施并经验评估这些机制。我们的实验结果表明，我们的机制确实可以降低实用性推理攻击的有效性，同时提供的实用性大大超过了原油差异的隐私基线。因此，我们的工作为保护数据集的全球性质的理论支持机制提供了基础。

学习多模式表示涉及从多个异构数据来源集成信息。这是一个充满挑战的重要领域，具有多媒体，情感计算，机器人，金融，人机互动和医疗保健的众多现实世界应用。不幸的是，多式化研究已经有限的资源研究（1）跨领域的概括和方式，（2）在训练和推理期间的复杂性，（3）嘈杂和缺少方式的鲁棒性。为了加速进展到深入的方式和任务，同时确保实现现实世界的稳健性，我们释放多麂，系统和统一的大规模基准，跨越15个数据集，10个模态，20个预测任务和6个研究领域。 Multibench提供自动端到端的机器学习管道，简化和标准化数据加载，实验设置和模型评估。为了使整体评价能够进行全博，提供综合方法，以评估（1）泛化，（2）时间和空间复杂度，以及（3）模型鲁棒性。 Multibench对未来的研究引入了积极的挑战，包括对大规模多模式数据集的可扩展性以及对现实缺陷的鲁棒性。要伴随该基准，我们还提供了多式化学习中的20个核心方法的标准化实现。简单地应用于不同研究领域提出的方法可以提高9/15数据集的最先进的性能。因此，Multibench介绍了一个里程碑，以统一多模式研究中的抗议努力，并铺平了更好地了解多式式模型的能力和限制，所有的易于使用，可访问性和再现性。将公开可用的多班，我们的标准化代码和排行榜将经常更新，并欢迎来自社区的投入。

Real-time individual endpoint prediction has always been a challenging task but of great clinic utility for both patients and healthcare providers. With 6,879 chronic kidney disease stage 4 (CKD4) patients as a use case, we explored the feasibility and performance of gated recurrent units with decay that models Weibull probability density function (GRU-D-Weibull) as a semi-parametric longitudinal model for real-time individual endpoint prediction. GRU-D-Weibull has a maximum C-index of 0.77 at 4.3 years of follow-up, compared to 0.68 achieved by competing models. The L1-loss of GRU-D-Weibull is ~66% of XGB(AFT), ~60% of MTLR, and ~30% of AFT model at CKD4 index date. The average absolute L1-loss of GRU-D-Weibull is around one year, with a minimum of 40% Parkes serious error after index date. GRU-D-Weibull is not calibrated and significantly underestimates true survival probability. Feature importance tests indicate blood pressure becomes increasingly important during follow-up, while eGFR and blood albumin are less important. Most continuous features have non-linear/parabola impact on predicted survival time, and the results are generally consistent with existing knowledge. GRU-D-Weibull as a semi-parametric temporal model shows advantages in built-in parameterization of missing, native support for asynchronously arrived measurement, capability of output both probability and point estimates at arbitrary time point for arbitrary prediction horizon, improved discrimination and point estimate accuracy after incorporating newly arrived data. Further research on its performance with more comprehensive input features, in-process or post-process calibration are warranted to benefit CKD4 or alike terminally-ill patients.

Humans use all of their senses to accomplish different tasks in everyday activities. In contrast, existing work on robotic manipulation mostly relies on one, or occasionally two modalities, such as vision and touch. In this work, we systematically study how visual, auditory, and tactile perception can jointly help robots to solve complex manipulation tasks. We build a robot system that can see with a camera, hear with a contact microphone, and feel with a vision-based tactile sensor, with all three sensory modalities fused with a self-attention model. Results on two challenging tasks, dense packing and pouring, demonstrate the necessity and power of multisensory perception for robotic manipulation: vision displays the global status of the robot but can often suffer from occlusion, audio provides immediate feedback of key moments that are even invisible, and touch offers precise local geometry for decision making. Leveraging all three modalities, our robotic system significantly outperforms prior methods.

Image analysis technologies empowered by artificial intelligence (AI) have proved images and videos to be an opportune source of data to learn about humpback whale (Megaptera novaeangliae) population sizes and dynamics. With the advent of social media, platforms such as YouTube present an abundance of video data across spatiotemporal contexts documenting humpback whale encounters from users worldwide. In our work, we focus on automating the classification of YouTube videos as relevant or irrelevant based on whether they document a true humpback whale encounter or not via deep learning. We use a CNN-RNN architecture pretrained on the ImageNet dataset for classification of YouTube videos as relevant or irrelevant. We achieve an average 85.7% accuracy, and 84.7% (irrelevant)/ 86.6% (relevant) F1 scores using five-fold cross validation for evaluation on the dataset. We show that deep learning can be used as a time-efficient step to make social media a viable source of image and video data for biodiversity assessments.

Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Such ML models can be used to produce both short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics (``climate''). Both of these tasks can be accomplished by employing a feedback loop, whereby the model is trained to predict forward one time step, then the trained model is iterated for multiple time steps with its output used as the input. In the absence of mitigating techniques, however, this technique can result in artificially rapid error growth, leading to inaccurate predictions and/or climate instability. In this article, we systematically examine the technique of adding noise to the ML model input during training as a means to promote stability and improve prediction accuracy. Furthermore, we introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training. Our case study uses reservoir computing, a machine-learning method using recurrent neural networks, to predict the spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find that reservoir computers trained with noise or with LMNT produce climate predictions that appear to be indefinitely stable and have a climate very similar to the true system, while reservoir computers trained without regularization are unstable. Compared with other types of regularization that yield stability in some cases, we find that both short-term and climate predictions from reservoir computers trained with noise or with LMNT are substantially more accurate. Finally, we show that the deterministic aspect of our LMNT regularization facilitates fast hyperparameter tuning when compared to training with noise.

Human and robot partners increasingly need to work together to perform tasks as a team. Robots designed for such collaboration must reason about how their task-completion strategies interplay with the behavior and skills of their human team members as they coordinate on achieving joint goals. Our goal in this work is to develop a computational framework for robot adaptation to human partners in human-robot team collaborations. We first present an algorithm for autonomously recognizing available task-completion strategies by observing human-human teams performing a collaborative task. By transforming team actions into low dimensional representations using hidden Markov models, we can identify strategies without prior knowledge. Robot policies are learned on each of the identified strategies to construct a Mixture-of-Experts model that adapts to the task strategies of unseen human partners. We evaluate our model on a collaborative cooking task using an Overcooked simulator. Results of an online user study with 125 participants demonstrate that our framework improves the task performance and collaborative fluency of human-agent teams, as compared to state of the art reinforcement learning methods.

这项研究提出了一个基于移动网格参数化的端到端无监督的差异可变形登记框架。使用此参数化，可以使用其转换雅各布的决定因素和末端速度场的卷曲来建模。变形场的新模型具有三个重要优势。首先，它放松了对成本函数的显式正则化项和相应重量的需求。平滑度隐含在溶液中，从而导致物理上合理的变形场。其次，它通过适用于转换雅各布决定因素的明确约束来保证差异性。最后，它适用于心脏数据处理，因为该参数化的性质是根据​​径向和旋转成分定义变形场。通过在包括2D和3D心脏MRI扫描在内的三个不同数据集上评估拟议方法来研究算法的有效性。结果表明，所提出的框架在生成差异变换的同时优于现有的基于学习的方法和基于非学习的方法。