我们示出了一种新的设计标准，即由加权标准规范化的子带错误的最小二乘范围，可用于概括比例型归一化子带自适应滤波（PTNSAF）框架。新标准直接惩罚子带错误，并包括使用阻尼正则牛顿的方法最小化的稀疏性惩罚术语。通过计算机模拟研究了所提出的广义PTNSAF（GPTNSAF）的影响。具体而言，我们研究了使用不同数量的子带和各种稀疏性惩罚术语对准稀疏，稀疏和分散系统的影响。结果表明，当目标系统是准稀疏或分散的时，增加子带数增加的损奋较大的效果。另一方面，对于稀疏的目标系统，促进稀疏性变得更加重要。更重要的是，这两个方面为GPTNSAF提供了互补和添加剂的益处，用于加速收敛。

最近在文献中使用的型号证明残留网络（RESNET）优于线性预测器实际上与已广泛用于计算机视觉中的标准EMER的不同。除了诸如标量标值输出或单个残差块的假设之外，这些模型在最终残余表示中没有非线性，其进入最终仿射层。为了编写非线性的这种差异，并揭示线性估计属性，我们通过简单地从标准试剂从标准试剂中的最后一个残留表示下丢弃非线性来定义reseness，即残余非线性估计器。我们展示了具有瓶颈块的宽雷峰可以始终保证标准的培训属性，标准的培训属性旨在实现，即添加更多块不会降低相同一组基本元素的性能。为了证明，我们首先识别雷峰是基本函数模型，其基于基础学习和线性预测的耦合问题受到限制。然后，为了从基础学习中解耦预测权重，我们构建一个特殊的架构被称为增强的resnest（a-resnest），这些架构始终保证在添加块中没有更糟糕的性能。结果，这种A-RES最终建立了使用相应基部的reS最低限制的低界限。我们的结果表明Resenss确实存在缩短功能重用的问题;然而，通过充分扩展或加宽输入空间，可以避免它，导致上述所需的性能。由已显示以优于ENRENET的DENSENETS的灵感，我们还提出了一种称为密集连接的非线性估算器（DENSENEST）的相应新模型。我们表明，任何Densenest都可以用瓶颈块表示为宽姓氏。与雷最多，Densenests在没有任何特殊建筑重新设计的情况下表现出理想的财产。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.

The latent space of autoencoders has been improved for clustering image data by jointly learning a t-distributed embedding with a clustering algorithm inspired by the neighborhood embedding concept proposed for data visualization. However, multivariate tabular data pose different challenges in representation learning than image data, where traditional machine learning is often superior to deep tabular data learning. In this paper, we address the challenges of learning tabular data in contrast to image data and present a novel Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS) algorithm by replacing t-distributions with multivariate Gaussian clusters. Unlike current methods, the proposed approach independently defines the Gaussian embedding and the target cluster distribution to accommodate any clustering algorithm in representation learning. A trained G-CEALS model extracts a quality embedding for unseen test data. Based on the embedding clustering accuracy, the average rank of the proposed G-CEALS method is 1.4 (0.7), which is superior to all eight baseline clustering and cluster embedding methods on seven tabular data sets. This paper shows one of the first algorithms to jointly learn embedding and clustering to improve multivariate tabular data representation in downstream clustering.

The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision. In this paper, we specifically investigate dimension reduction for time series through functional data analysis. Current methods for dimension reduction in functional data are functional principal component analysis and functional autoencoders, which are limited to linear mappings or scalar representations for the time series, which is inefficient. In real data applications, the nature of the data is much more complex. We propose a non-linear function-on-function approach, which consists of a functional encoder and a functional decoder, that uses continuous hidden layers consisting of continuous neurons to learn the structure inherent in functional data, which addresses the aforementioned concerns in the existing approaches. Our approach gives a low dimension latent representation by reducing the number of functional features as well as the timepoints at which the functions are observed. The effectiveness of the proposed model is demonstrated through multiple simulations and real data examples.

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-balanced Re-weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 \& V6 show the performances and generality of the SCR with the traditional SGG models.

In this paper we discuss the theory used in the design of an open source lightmorphic signatures analysis toolkit (LSAT). In addition to providing a core functionality, the software package enables specific optimizations with its modular and customizable design. To promote its usage and inspire future contributions, LSAT is publicly available. By using a self-supervised neural network and augmented machine learning algorithms, LSAT provides an easy-to-use interface with ample documentation. The experiments demonstrate that LSAT improves the otherwise tedious and error-prone tasks of translating lightmorphic associated data into usable spectrograms, enhanced with parameter tuning and performance analysis. With the provided mathematical functions, LSAT validates the nonlinearity encountered in the data conversion process while ensuring suitability of the forecasting algorithms.

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have been proposed recently to identify those changes, they still suffer from missing subtle changes, poor scalability, or/and sensitive to noise points. To meet these challenges, we are the first to generalise the CPD problem as a special case of the Change-Interval Detection (CID) problem. Then we propose a CID method, named iCID, based on a recent Isolation Distributional Kernel (IDK). iCID identifies the change interval if there is a high dissimilarity score between two non-homogeneous temporal adjacent intervals. The data-dependent property and finite feature map of IDK enabled iCID to efficiently identify various types of change points in data streams with the tolerance of noise points. Moreover, the proposed online and offline versions of iCID have the ability to optimise key parameter settings. The effectiveness and efficiency of iCID have been systematically verified on both synthetic and real-world datasets.