Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural methods achieved impressive results in link prediction and complex query answering tasks by learning representations of entities, relations, and queries. Still, most existing query answering methods rely on transductive entity embeddings and cannot generalize to KGs containing new entities without retraining the entity embeddings. In this work, we study the inductive query answering task where inference is performed on a graph containing new entities with queries over both seen and unseen entities. To this end, we devise two mechanisms leveraging inductive node and relational structure representations powered by graph neural networks (GNNs). Experimentally, we show that inductive models are able to perform logical reasoning at inference time over unseen nodes generalizing to graphs up to 500% larger than training ones. Exploring the efficiency--effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes. Code is available at https://github.com/DeepGraphLearning/InductiveQE.

沙尘暴可能会显着降低火星轨道轨道轨道的成像质量，并延迟绘制全球地形和地貌学的进度。为了解决这个问题，本文提出了一种方法，可以重用地球上获得的图像去除图像，以解决火星上的灰尘回避问题。在这种方法中，我们收集了Tianwen-1捕获的遥感图像，并手动选择了数百个干净和灰尘的图像。受到地球上雾兹的形成过程的启发，我们在干净的图像上制定了类似的视觉降解过程，并合成尘土飞扬的图像，与逼真的尘土飞扬的图像共享类似的特征分布。这些逼真的清洁和合成的尘土图像对用于训练一个固有地编码灰尘无关的特征并将它们解码为无尘图像的深层模型。定性和定量结果表明，拟议方法可以有效消除沙尘暴，从而显然改善了火星的地形和地貌细节。

知识图表（kgs）以头部关系的形式捕获知识 - 尾部三元组，是许多AI系统中的重要组成部分。 KGS上有两个重要的推理任务：（1）单跳知识图完成，涉及预测公斤中的各个环节; （2），多跳推理，目标是预测哪个kg实体满足给定的逻辑查询。基于嵌入的方法通过首先计算每个实体和关系的嵌入来解决两个任务，然后使用它们形成预测。但是，现有可扩展的KG嵌入框架仅支持单跳知识图完成，并且不能应用于更具挑战性的多跳推理任务。在这里，我们呈现可扩展的多跳推理（SMORE），这是KGS中单跳和多跳推理的第一个总框架。使用单机略微闪烁可以在FreeBase KG（86米实体，338M边缘）中执行多跳推理，比以前考虑的KGs大1,500倍。粉刷运行时性能的关键是一种新的双向抑制采样，实现了在线培训数据生成的复杂性的平方根降低。此外，SMORE利用异步调度，基于CPU的数据采样，基于GPU的嵌入计算和频繁CPU - GPU IO。 Smore通过2.2倍提高了82倍的吞吐量（即，训练速度），以最小的GPU存储器要求（2GB用于训练86M节点FreeBase上的400微米嵌入），并达到与GPU的数量接近线性加速。此外，在更简单的单跳知识图形完成任务中，Smore实现了对单个GPU和多GPU设置的最先进框架的可比或更好的运行时间性能。

AI正在经历范式转变，随着模型的兴起（例如Bert，Dall-E，GPT-3），这些模型经过大规模的数据训练，并且可以适应广泛的下游任务。我们称这些模型基础模型来强调其至关重要但不完整的特征。该报告提供了基础模型的机会和风险的详尽说明，包括其功能（例如语言，愿景，机器人技术，推理，人类互动）和技术原则（例如，模型架构，培训程序，数据，系统，安全，安全性，评估，理论）对其应用（例如法律，医疗保健，教育）和社会影响（例如不平等，滥用，经济和环境影响，法律和道德考虑）。尽管基础模型基于标准的深度学习和转移学习，但它们的规模导致了新的新兴能力，以及它们在许多任务中的有效性都激发了同质化。同质化提供了强大的杠杆作用，但要求谨慎，因为基础模型的缺陷均由下游的所有适应模型继承。尽管即将广泛地部署基础模型，但我们目前对它们的工作方式，失败以及由于其新兴属性的影响而缺乏清晰的了解。为了解决这些问题，我们认为基础模型的许多批判性研究都需要与他们的基本社会技术性质相称。

使用从预先接受训练的语言模型（LMS）和知识图表（LMS）和知识图表（kgs）回答问题的问题提出了两个挑战：给定QA上下文（问答选择），方法需要（i）从大型千克识别相关知识，（ii）对QA上下文和kg进行联合推理。在这项工作中，我们提出了一种新的模型，QA-GNN，它通过两个关键创新解决了上述挑战：（i）相关评分，我们使用LMS来估计KG节点相对于给定的QA上下文的重要性，以及（ii）联合推理，我们将QA上下文和kg连接到联合图，并通过图形神经网络相互更新它们的表示。我们评估了QA基准的模型（CommanSeaseQA，OpenBookQA）和生物医学（MedQa-USMLE）域名。QA-GNN优于现有的LM和LM + kg模型，并表现出可解释和结构化推理的能力，例如，正确处理问题的否定。

We present the OPEN GRAPH BENCHMARK (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders, evaluation scripts, baseline code, and leaderboards are publicly available at https://ogb.stanford.edu.

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

Projection operations are a typical computation bottleneck in online learning. In this paper, we enable projection-free online learning within the framework of Online Convex Optimization with Memory (OCO-M) -- OCO-M captures how the history of decisions affects the current outcome by allowing the online learning loss functions to depend on both current and past decisions. Particularly, we introduce the first projection-free meta-base learning algorithm with memory that minimizes dynamic regret, i.e., that minimizes the suboptimality against any sequence of time-varying decisions. We are motivated by artificial intelligence applications where autonomous agents need to adapt to time-varying environments in real-time, accounting for how past decisions affect the present. Examples of such applications are: online control of dynamical systems; statistical arbitrage; and time series prediction. The algorithm builds on the Online Frank-Wolfe (OFW) and Hedge algorithms. We demonstrate how our algorithm can be applied to the online control of linear time-varying systems in the presence of unpredictable process noise. To this end, we develop the first controller with memory and bounded dynamic regret against any optimal time-varying linear feedback control policy. We validate our algorithm in simulated scenarios of online control of linear time-invariant systems.

This paper presents a practical global optimization algorithm for the K-center clustering problem, which aims to select K samples as the cluster centers to minimize the maximum within-cluster distance. This algorithm is based on a reduced-space branch and bound scheme and guarantees convergence to the global optimum in a finite number of steps by only branching on the regions of centers. To improve efficiency, we have designed a two-stage decomposable lower bound, the solution of which can be derived in a closed form. In addition, we also propose several acceleration techniques to narrow down the region of centers, including bounds tightening, sample reduction, and parallelization. Extensive studies on synthetic and real-world datasets have demonstrated that our algorithm can solve the K-center problems to global optimal within 4 hours for ten million samples in the serial mode and one billion samples in the parallel mode. Moreover, compared with the state-of-the-art heuristic methods, the global optimum obtained by our algorithm can averagely reduce the objective function by 25.8% on all the synthetic and real-world datasets.

Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations. For real-world applications of RL, recovering underlying latent states is crucial, particularly when sensory inputs contain irrelevant and exogenous information. In this work, we study how information bottlenecks can be used to construct latent states efficiently in the presence of task-irrelevant information. We propose architectures that utilize variational and discrete information bottlenecks, coined as RepDIB, to learn structured factorized representations. Exploiting the expressiveness bought by factorized representations, we introduce a simple, yet effective, bottleneck that can be integrated with any existing self-supervised objective for RL. We demonstrate this across several online and offline RL benchmarks, along with a real robot arm task, where we find that compressed representations with RepDIB can lead to strong performance improvements, as the learned bottlenecks help predict only the relevant state while ignoring irrelevant information.