班级不平衡问题是许多现实世界中的机器学习任务的固有，尤其是对于罕见的事实分类问题。尽管数据不平衡的影响和处理是广为人知的，但度量标准对阶级失衡的敏感性的幅度很少引起关注。结果，敏感的指标通常被忽略，而其敏感性可能只有边际。在本文中，我们介绍了一个直观的评估框架，该框架量化了指标对类不平衡的敏感性。此外，我们揭示了一个有趣的事实，即指标的敏感性存在对数行为，这意味着较高的失衡比与指标的较低灵敏度有关。我们的框架建立了对阶级不平衡对指标的影响的直观理解。我们认为，这可以帮助避免许多常见的错误，特别是强调和错误的假设，即在不同的级别不平衡比率下所有指标的数量都是可比的。

太阳耀斑不仅对外层空间的技术和宇航员的健康构成风险，而且还会在我们的高科技，相互联系的基础设施中造成破坏我们的生活。尽管已经提出了许多机器学习方法来改善耀斑预测，但据我们所知，它们都没有研究过异常值对可靠性和这些模型的性能的影响。在这项研究中，我们研究了异常值在多元时间序列基准数据集中的影响，即天鹅 - SF对耀斑预测模型，并检验我们的假设。也就是说，Swan-SF中存在异常值，将其删除增强了看不见的数据集上预测模型的性能。我们采用隔离森林来检测弱耀斑实例之间的异常值。使用大量污染速率进行了几项实验，这些污染速率确定了当前异常值的百分比。我们使用LimeseriessVC来评估每个数据集的实际污染质量。在我们最好的发现中，我们的真实技能统计数据增加了279％，海德克技能得分提高了68％。结果表明，如果检测到并正确删除异常值，总体上可以取得重大改进来爆发预测。

In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent a special case of recurrent neural networks. In this paper we 1) formalize the learning problem for stochastic LTI systems with inputs, 2) derive a PAC-Bayesian-Like error bound for such systems, 3) discuss various consequences of this error bound.

The aim of this study is to define importance of predictors for black box machine learning methods, where the prediction function can be highly non-additive and cannot be represented by statistical parameters. In this paper we defined a ``Generalized Variable Importance Metric (GVIM)'' using the true conditional expectation function for a continuous or a binary response variable. We further showed that the defined GVIM can be represented as a function of the Conditional Average Treatment Effect (CATE) squared for multinomial and continuous predictors. Then we propose how the metric can be estimated using using any machine learning models. Finally we showed the properties of the estimator using multiple simulations.

预测性编码（PC）是计算神经科学中的有影响力的理论，它认为皮层通过实施层次结构的预测误差最小化过程来形成无监督的世界模型。 PC网络（PCN）分为两个阶段。首先，更新神经活动以优化网络对外部刺激的反应。其次，更新突触权重以整合活动中的这种变化 - 一种称为\ emph {前瞻性配置}的算法。虽然先前的工作已经显示了如何在各种限制下发现近似倒流（BP），但最近的工作表明，在该标准制度中运行的PCN不近似BP，但仍获得了竞争性培训和广泛性培训，以进行BP训练。网络在诸如在线，几乎没有射击和持续学习之类的任务上的网络效果超过了它们，在该任务中，大脑擅长于大脑。尽管这种有希望的经验表现，但理论上对PCN的性质和动力学在该制度中的理解很少。在本文中，我们对经过预期配置训练的PCN的性质进行了全面的理论分析。我们首先得出有关PCN的推理平衡以及与目标传播（TP）的紧密联系关系的分析结果。其次，我们提供了PCN中学习的理论分析，作为广义期望最大化的变体，并使用它来证明PCN与BP损耗函数的关键点的收敛性，从而表明，从理论上讲，深色PCN可以实现相同的实现。作为BP的概括性能，同时保持其独特的优势。

文献中已经提出了许多关联记忆的神经网络模型。其中包括经典的Hopfield网络（HNS），稀疏分布式记忆（SDM）以及最近的现代连续Hopfield网络（MCHN），该网络在机器学习中具有与自我注意力的紧密联系。在本文中，我们提出了一个通用框架，以理解此类内存网络的操作，例如三个操作的顺序：相似性，分离和投影。我们将所有这些记忆模型作为我们的一般框架的实例，具有不同的相似性和分离函数。我们将Krotov等人（2020）的数学框架扩展到使用神经元之间仅具有二阶相互作用的神经网络动力学来表达通用的关联存储模型，并得出了一种通用能量函数，该函数是动力学的lyapunov函数。最后，使用我们的框架，我们从经验上研究了这些关联记忆模型使用不同相似性函数的能力，超出了点产品相似性度量，并从经验上证明了欧几里得或曼哈顿距离距离相似性指标在实践中在许多任务中表现出色，从而启用了一项启用一项效果比现有模型更强大的检索和更高的内存能力。

The standard recurrent neural network language model (rnnlm) generates sentences one word at a time and does not work from an explicit global sentence representation. In this work, we introduce and study an rnn-based variational autoencoder generative model that incorporates distributed latent representations of entire sentences. This factorization allows it to explicitly model holistic properties of sentences such as style, topic, and high-level syntactic features. Samples from the prior over these sentence representations remarkably produce diverse and well-formed sentences through simple deterministic decoding. By examining paths through this latent space, we are able to generate coherent novel sentences that interpolate between known sentences. We present techniques for solving the difficult learning problem presented by this model, demonstrate its effectiveness in imputing missing words, explore many interesting properties of the model's latent sentence space, and present negative results on the use of the model in language modeling.

Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets. This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.