神经网络是强大的功能估计器，导致其作为建模结构化数据的首选范式的地位。但是，与其他强调问题模块化的结构化表示不同，例如因子图 - 神经网络通常是从输入到输出的单片映射，并具有固定的计算顺序。这种限制阻止他们捕获模型变量之间的不同计算方向和相互作用。在本文中，我们结合了因子图和神经网络的代表性强度，提出了无向神经网络（UNNS）：一个灵活的框架，用于指定可以按任何顺序执行的计算。对于特定的选择，我们提出的模型集成并扩展了许多现有的架构：带有隐式层的馈电，经常性，自我发项网络，自动编码器和网络。我们在一系列任务上展示了无方向性的神经体系结构的有效性：受树约束依赖性解析，卷积图像分类和序列完成。通过改变计算顺序，我们展示了如何同时将单个UNN用作分类器和原型发生器，以及它如何填充输入序列的缺失部分，从而使它们成为进一步研究的有希望的领域。

在本文中，我们试图通过引入深度学习模型的句法归纳偏见来建立两所学校之间的联系。我们提出了两个归纳偏见的家族，一个家庭用于选区结构，另一个用于依赖性结构。选区归纳偏见鼓励深度学习模型使用不同的单位（或神经元）分别处理长期和短期信息。这种分离为深度学习模型提供了一种方法，可以从顺序输入中构建潜在的层次表示形式，即更高级别的表示由高级表示形式组成，并且可以分解为一系列低级表示。例如，在不了解地面实际结构的情况下，我们提出的模型学会通过根据其句法结构组成变量和运算符的表示来处理逻辑表达。另一方面，依赖归纳偏置鼓励模型在输入序列中找到实体之间的潜在关系。对于自然语言，潜在关系通常被建模为一个定向依赖图，其中一个单词恰好具有一个父节点和零或几个孩子的节点。将此约束应用于类似变压器的模型之后，我们发现该模型能够诱导接近人类专家注释的有向图，并且在不同任务上也优于标准变压器模型。我们认为，这些实验结果为深度学习模型的未来发展展示了一个有趣的选择。

通常通过将许多输入张量汇总为单个表示形式来处理神经网络中神经网络中的处理集或其他无序的，潜在的变化大小的输入。尽管从简单的汇总到多头关注已经存在许多聚合方法，但从理论和经验的角度来看，它们的代表力都受到限制。在搜索主要功能更强大的聚合策略时，我们提出了一种基于优化的方法，称为平衡聚​​集。我们表明，许多现有的聚合方法可以作为平衡聚集的特殊情况恢复，并且在某些重要情况下，它效率更高。在许多现有的架构和应用中，平衡聚集可以用作置换式替换。我们在三个不同的任务上验证其效率：中值估计，班级计数和分子性质预测。在所有实验中，平衡聚集的性能都比我们测试的其他聚合技术更高。

我用Hunglish2语料库训练神经电脑翻译任务的模型。这项工作的主要贡献在培训NMT模型期间评估不同的数据增强方法。我提出了5种不同的增强方法，这些方法是结构感知的，这意味着而不是随机选择用于消隐或替换的单词，句子的依赖树用作增强的基础。我首先关于神经网络的详细文献综述，顺序建模，神经机翻译，依赖解析和数据增强。经过详细的探索性数据分析和Hunglish2语料库的预处理之后，我使用所提出的数据增强技术进行实验。匈牙利语的最佳型号达到了33.9的BLEU得分，而英国匈牙利最好的模型达到了28.6的BLEU得分。

Pre-publication draft of a book to be published byMorgan & Claypool publishers. Unedited version released with permission. All relevant copyrights held by the author and publisher extend to this pre-publication draft.

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.

These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics. The main objective of these notes is to introduce a student who is familiar with concepts in linear algebra and partial differential equations to select topics in deep learning. These lecture notes exploit the strong connections between deep learning algorithms and the more conventional techniques of computational physics to achieve two goals. First, they use concepts from computational physics to develop an understanding of deep learning algorithms. Not surprisingly, many concepts in deep learning can be connected to similar concepts in computational physics, and one can utilize this connection to better understand these algorithms. Second, several novel deep learning algorithms can be used to solve challenging problems in computational physics. Thus, they offer someone who is interested in modeling a physical phenomena with a complementary set of tools.

Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BIGBIRD, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that BIGBIRD is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, BIGBIRD drastically improves performance on various NLP tasks such as question answering and summarization. We also propose novel applications to genomics data.

这是一门专门针对STEM学生开发的介绍性机器学习课程。我们的目标是为有兴趣的读者提供基础知识，以在自己的项目中使用机器学习，并将自己熟悉术语作为进一步阅读相关文献的基础。在这些讲义中，我们讨论受监督，无监督和强化学习。注释从没有神经网络的机器学习方法的说明开始，例如原理分析，T-SNE，聚类以及线性回归和线性分类器。我们继续介绍基本和先进的神经网络结构，例如密集的进料和常规神经网络，经常性的神经网络，受限的玻尔兹曼机器，（变性）自动编码器，生成的对抗性网络。讨论了潜在空间表示的解释性问题，并使用梦和对抗性攻击的例子。最后一部分致力于加强学习，我们在其中介绍了价值功能和政策学习的基本概念。

这项正在进行的工作旨在为统计学习提供统一的介绍，从诸如GMM和HMM等经典模型到现代神经网络（如VAE和扩散模型）缓慢地构建。如今，有许多互联网资源可以孤立地解释这一点或新的机器学习算法，但是它们并没有（也不能在如此简短的空间中）将这些算法彼此连接起来，或者与统计模型的经典文献相连现代算法出现了。同样明显缺乏的是一个单一的符号系统，尽管对那些已经熟悉材料的人（如这些帖子的作者）不满意，但对新手的入境造成了重大障碍。同样，我的目的是将各种模型（尽可能）吸收到一个用于推理和学习的框架上，表明（以及为什么）如何以最小的变化将一个模型更改为另一个模型（其中一些是新颖的，另一些是文献中的）。某些背景当然是必要的。我以为读者熟悉基本的多变量计算，概率和统计以及线性代数。这本书的目标当然不是​​完整性，而是从基本知识到过去十年中极强大的新模型的直线路径或多或少。然后，目标是补充而不是替换，诸如Bishop的\ emph {模式识别和机器学习}之类的综合文本，该文本现在已经15岁了。

深度学习使用由其重量进行参数化的神经网络。通常通过调谐重量来直接最小化给定损耗功能来训练神经网络。在本文中，我们建议将权重重新参数转化为网络中各个节点的触发强度的目标。给定一组目标，可以计算使得发射强度最佳地满足这些目标的权重。有人认为，通过我们称之为级联解压缩的过程，使用培训的目标解决爆炸梯度的问题，并使损失功能表面更加光滑，因此导致更容易，培训更快，以及潜在的概括，神经网络。它还允许更容易地学习更深层次和经常性的网络结构。目标对重量的必要转换有额外的计算费用，这是在许多情况下可管理的。在目标空间中学习可以与现有的神经网络优化器相结合，以额外收益。实验结果表明了使用目标空间的速度，以及改进的泛化的示例，用于全连接的网络和卷积网络，以及调用和处理长时间序列的能力，并使用经常性网络进行自然语言处理。

大多数设置深度学习的预测模型，使用Set-Scifariant操作，但它们实际上在MultiSet上运行。我们表明设置的函数不能代表多种功能上的某些功能，因此我们介绍了更适当的多种式概念概念。我们确定现有的深度设置预测网络（DSPN）可以是多机构的，而不会被设定的标准规模阻碍，并通过近似隐式差分改进它，允许更好地优化，同时更快和节省存储器。在一系列玩具实验中，我们表明，多机构的角度是有益的，在大多数情况下，我们对DSPN的变化达到了更好的结果。关于CLEVR对象性质预测，由于通过隐含分化所取得的益处，我们在最先进的评估指标中从8％到77％的最先进的槽注意力从8％提高到77％。

为了在深度学习中解释隐性正则化时，给予了矩阵和张量因子化的突出重点，这与简化的神经网络相对应。结果表明，这些模型分别表现出对低基质和张量排名的隐式趋势。当前的论文理论上绘制了更接近实际的深度学习，从理论上分析了分层张分解中的隐式正则化，该模型等同于某些深卷积神经网络。通过动态系统镜头，我们克服了与层次结构相关的挑战，并建立了对低层次张量级别的隐性正则化。这转化为相关卷积网络对区域的隐性正则化。受我们的理论的启发，我们设计了明确的正则化，阻碍了区域性，并证明了其在需要建筑变化的传统智慧的情况下，可以改善现代卷积网络在非本地任务上的性能。我们的工作突出了通过对其隐式正则化的理论分析来增强神经网络的潜力。

Multilayer Neural Networks trained with the backpropagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture, Gradient-Based Learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques.Real-life document recognition systems are composed of multiple modules including eld extraction, segmentation, recognition, and language modeling. A new learning paradigm, called Graph Transformer Networks (GTN), allows such multi-module systems to be trained globally using Gradient-Based methods so as to minimize an overall performance measure.Two systems for on-line handwriting recognition are described. Experiments demonstrate the advantage of global training, and the exibility of Graph Transformer Networks.A Graph Transformer Network for reading bank check is also described. It uses Convolutional Neural Network character recognizers combined with global training techniques to provides record accuracy on business and personal checks. It is deployed commercially and reads several million checks per day.

Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variables. Inference consists in clamping the value of observed variables and finding configurations of the remaining variables that minimize the energy. Learning consists in finding an energy function in which observed configurations of the variables are given lower energies than unobserved ones. The EBM approach provides a common theoretical framework for many learning models, including traditional discriminative and generative approaches, as well as graph-transformer networks, conditional random fields, maximum margin Markov networks, and several manifold learning methods.Probabilistic models must be properly normalized, which sometimes requires evaluating intractable integrals over the space of all possible variable configurations. Since EBMs have no requirement for proper normalization, this problem is naturally circumvented. EBMs can be viewed as a form of non-probabilistic factor graphs, and they provide considerably more flexibility in the design of architectures and training criteria than probabilistic approaches.

人工智能代理必须从周围环境中学到学习，并了解所学习的知识，以便做出决定。虽然从数据的最先进的学习通常使用子符号分布式表示，但是使用用于知识表示的一阶逻辑语言，推理通常在更高的抽象级别中有用。结果，将符号AI和神经计算结合成神经符号系统的尝试已经增加。在本文中，我们呈现了逻辑张量网络（LTN），一种神经组织形式和计算模型，通过引入许多值的端到端可分别的一阶逻辑来支持学习和推理，称为真实逻辑作为表示语言深入学习。我们表明LTN为规范提供了统一的语言，以及多个AI任务的计算，如数据聚类，多标签分类，关系学习，查询应答，半监督学习，回归和嵌入学习。我们使用TensorFlow2的许多简单的解释例实施和说明上述每个任务。关键词：神经组音恐怖症，深度学习和推理，许多值逻辑。

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.

Graph neural networks (GNNs) are widely used for modeling complex interactions between entities represented as vertices of a graph. Despite recent efforts to theoretically analyze the expressive power of GNNs, a formal characterization of their ability to model interactions is lacking. The current paper aims to address this gap. Formalizing strength of interactions through an established measure known as separation rank, we quantify the ability of certain GNNs to model interaction between a given subset of vertices and its complement, i.e. between sides of a given partition of input vertices. Our results reveal that the ability to model interaction is primarily determined by the partition's walk index -- a graph-theoretical characteristic that we define by the number of walks originating from the boundary of the partition. Experiments with common GNN architectures corroborate this finding. As a practical application of our theory, we design an edge sparsification algorithm named Walk Index Sparsification (WIS), which preserves the ability of a GNN to model interactions when input edges are removed. WIS is simple, computationally efficient, and markedly outperforms alternative methods in terms of induced prediction accuracy. More broadly, it showcases the potential of improving GNNs by theoretically analyzing the interactions they can model.

即使机器学习算法已经在数据科学中发挥了重要作用，但许多当前方法对输入数据提出了不现实的假设。由于不兼容的数据格式，或数据集中的异质，分层或完全缺少的数据片段，因此很难应用此类方法。作为解决方案，我们提出了一个用于样本表示，模型定义和培训的多功能，统一的框架，称为“ Hmill”。我们深入审查框架构建和扩展的机器学习的多个范围范式。从理论上讲，为HMILL的关键组件的设计合理，我们将通用近似定理的扩展显示到框架中实现的模型所实现的所有功能的集合。本文还包含有关我们实施中技术和绩效改进的详细讨论，该讨论将在MIT许可下发布供下载。该框架的主要资产是其灵活性，它可以通过相同的工具对不同的现实世界数据源进行建模。除了单独观察到每个对象的一组属性的标准设置外，我们解释了如何在框架中实现表示整个对象系统的图表中的消息推断。为了支持我们的主张，我们使用框架解决了网络安全域的三个不同问题。第一种用例涉及来自原始网络观察结果的IoT设备识别。在第二个问题中，我们研究了如何使用以有向图表示的操作系统的快照可以对恶意二进制文件进行分类。最后提供的示例是通过网络中实体之间建模域黑名单扩展的任务。在所有三个问题中，基于建议的框架的解决方案可实现与专业方法相当的性能。

A large amount of recent research has the far-reaching goal of finding training methods for deep neural networks that can serve as alternatives to backpropagation (BP). A prominent example is predictive coding (PC), which is a neuroscience-inspired method that performs inference on hierarchical Gaussian generative models. These methods, however, fail to keep up with modern neural networks, as they are unable to replicate the dynamics of complex layers and activation functions. In this work, we solve this problem by generalizing PC to arbitrary probability distributions, enabling the training of architectures, such as transformers, that are hard to approximate with only Gaussian assumptions. We perform three experimental analyses. First, we study the gap between our method and the standard formulation of PC on multiple toy examples. Second, we test the reconstruction quality on variational autoencoders, where our method reaches the same reconstruction quality as BP. Third, we show that our method allows us to train transformer networks and achieve a performance comparable with BP on conditional language models. More broadly, this method allows neuroscience-inspired learning to be applied to multiple domains, since the internal distributions can be flexibly adapted to the data, tasks, and architectures used.