在过去十年中，已经开发出新的深度学习（DL）算法，工作负载和硬件来解决各种问题。尽管工作量和硬件生态系统的进步，DL系统的编程方法是停滞不前的。 DL工作负载从DL库中的高度优化，特定于平台和不灵活的内核，或者在新颖的操作员的情况下，通过具有强大性能的DL框架基元建立参考实现。这项工作介绍了Tensor加工基元（TPP），一个编程抽象，用于高效的DL工作负载的高效，便携式实现。 TPPS定义了一组紧凑而多才多艺的2D张镜操作员（或虚拟张量ISA），随后可以用作构建块，以在高维张量上构建复杂的运算符。 TPP规范是平台 - 不可行的，因此通过TPPS表示的代码是便携式的，而TPP实现是高度优化的，并且特定于平台。我们展示了我们使用独立内核和端到端DL＆HPC工作负载完全通过TPPS表达的方法的效力和生存性，这在多个平台上优于最先进的实现。

Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks like, node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network (GCN), graph attention network (GAT), graph autoencoder (GAE), and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field.

Learning style refers to a type of training mechanism adopted by an individual to gain new knowledge. As suggested by the VARK model, humans have different learning preferences like visual, auditory, etc., for acquiring and effectively processing information. Inspired by this concept, our work explores the idea of mixed information sharing with model compression in the context of Knowledge Distillation (KD) and Mutual Learning (ML). Unlike conventional techniques that share the same type of knowledge with all networks, we propose to train individual networks with different forms of information to enhance the learning process. We formulate a combined KD and ML framework with one teacher and two student networks that share or exchange information in the form of predictions and feature maps. Our comprehensive experiments with benchmark classification and segmentation datasets demonstrate that with 15% compression, the ensemble performance of networks trained with diverse forms of knowledge outperforms the conventional techniques both quantitatively and qualitatively.

We present a robust methodology for evaluating biases in natural language generation(NLG) systems. Previous works use fixed hand-crafted prefix templates with mentions of various demographic groups to prompt models to generate continuations for bias analysis. These fixed prefix templates could themselves be specific in terms of styles or linguistic structures, which may lead to unreliable fairness conclusions that are not representative of the general trends from tone varying prompts. To study this problem, we paraphrase the prompts with different syntactic structures and use these to evaluate demographic bias in NLG systems. Our results suggest similar overall bias trends but some syntactic structures lead to contradictory conclusions compared to past works. We show that our methodology is more robust and that some syntactic structures prompt more toxic content while others could prompt less biased generation. This suggests the importance of not relying on a fixed syntactic structure and using tone-invariant prompts. Introducing syntactically-diverse prompts can achieve more robust NLG (bias) evaluation.

Learning rich skills through temporal abstractions without supervision of external rewards is at the frontier of Reinforcement Learning research. Existing works mainly fall into two distinctive categories: variational and Laplacian-based option discovery. The former maximizes the diversity of the discovered options through a mutual information loss but overlooks coverage of the state space, while the latter focuses on improving the coverage of options by increasing connectivity during exploration, but does not consider diversity. In this paper, we propose a unified framework that quantifies diversity and coverage through a novel use of the Determinantal Point Process (DPP) and enables unsupervised option discovery explicitly optimizing both objectives. Specifically, we define the DPP kernel matrix with the Laplacian spectrum of the state transition graph and use the expected mode number in the trajectories as the objective to capture and enhance both diversity and coverage of the learned options. The proposed option discovery algorithm is extensively evaluated using challenging tasks built with Mujoco and Atari, demonstrating that our proposed algorithm substantially outperforms SOTA baselines from both diversity- and coverage-driven categories. The codes are available at https://github.com/LucasCJYSDL/ODPP.

Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object. Object-nav has been extensively studied by the Embodied-AI community, but most solutions are often restricted to considering static objects (e.g., television, fridge, etc.). We propose a modular framework for object-nav that is able to efficiently search indoor environments for not just static objects but also movable objects (e.g. fruits, glasses, phones, etc.) that frequently change their positions due to human intervention. Our contextual-bandit agent efficiently explores the environment by showing optimism in the face of uncertainty and learns a model of the likelihood of spotting different objects from each navigable location. The likelihoods are used as rewards in a weighted minimum latency solver to deduce a trajectory for the robot. We evaluate our algorithms in two simulated environments and a real-world setting, to demonstrate high sample efficiency and reliability.

Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.

最近已证明，平均场控制（MFC）是可扩展的工具，可近似解决大规模的多代理增强学习（MARL）问题。但是，这些研究通常仅限于无约束的累积奖励最大化框架。在本文中，我们表明，即使在存在约束的情况下，也可以使用MFC方法近似MARL问题。具体来说，我们证明，一个$ n $ agent的约束MARL问题，以及每个尺寸的尺寸$ | \ Mathcal {x} | $和$ | \ Mathcal {u} | $的状态和操作空间，可以通过与错误相关的约束MFC问题近似，$ e \ triangleq \ Mathcal {o} \ left（[\ sqrt {| \ Mathcal {| \ Mathcal {x} |} |}+\ sqrt {| ]/\ sqrt {n} \ right）$。在奖励，成本和状态过渡功能独立于人口的行动分布的特殊情况下，我们证明该错误可以将错误提高到$ e = \ nathcal {o}（\ sqrt {| | \ Mathcal {x x x } |}/\ sqrt {n}）$。另外，我们提供了一种基于自然策略梯度的算法，并证明它可以在$ \ Mathcal {o}（e）$的错误中解决受约束的MARL问题，并具有$ \ MATHCAL {O}的样本复杂性（E^{ - e^{ - 6}）$。

在包括搜索在内的各种应用程序中，积极消费数字文档的研究范围为研究范围。传统上，文档中的搜索是作为文本匹配的问题施放的，忽略了结构化文档，表格等中常见的丰富布局和视觉提示。为此，我们提出了一个大多数未探索的问题：“我们可以搜索其他类似的snippets在目标文档页面中存在给定文档摘要的单个查询实例吗？”。我们建议单体将其作为单拍的摘要检测任务解决。单体融合了摘要和文档的视觉，文本和空间方式的上下文，以在目标文档中找到查询片段。我们进行了广泛的消融和实验，显示单体从一击对象检测（BHRL），模板匹配和文档理解（Layoutlmv3）中优于几个基线。由于目前的任务缺乏相关数据，因此我们对单体进行了编程生成的数据训练，该数据具有许多视觉上相似的查询片段和来自两个数据集的目标文档对 - Flamingo表单和PublayNet。我们还进行人类研究以验证生成的数据。

我们表明，在合作$ n $ n $ agent网络中，可以为代理设计本地可执行的策略，以使所得的平均奖励（值）的折现总和非常接近于计算出的最佳价值（包括非本地）策略。具体而言，我们证明，如果$ | \ MATHCAL {X} |，| \ MATHCAL {U} | $表示状态大小和单个代理的操作空间，那么对于足够小的折现因子，近似错误，则由$ \ MATHCAL {o}（e）$ where $ e \ triangleq \ frac {1} {\ sqrt {n}}} \ left [\ sqrt {\ sqrt {| \ Mathcal {x}} |} |} |} |}+\ sqrt { } |} \ right] $。此外，在一种特殊情况下，奖励和状态过渡功能独立于人口的行动分布，错误将$ \ nathcal {o}（e）$提高到其中$ e \ e \ triangleq \ frac {1} {\ sqrt {\ sqrt {n}} \ sqrt {| \ Mathcal {x} |} $。最后，我们还设计了一种算法来明确构建本地政策。在我们的近似结果的帮助下，我们进一步确定构建的本地策略在$ \ Mathcal {o}（\ max \ {e，\ epsilon \}）$最佳策略的距离之内对于任何$ \ epsilon> 0 $，本地策略是$ \ MATHCAL {O}（\ Epsilon^{ - 3}）$。