While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent neurons, the latter work better for training multi-layer neural networks. This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of the hard non-linearity and non-differentiability at zero, creating sparse representations with true zeros, which seem remarkably suitable for naturally sparse data. Even though they can take advantage of semi-supervised setups with extra-unlabeled data, deep rectifier networks can reach their best performance without requiring any unsupervised pre-training on purely supervised tasks with large labeled datasets. Hence, these results can be seen as a new milestone in the attempts at understanding the difficulty in training deep but purely supervised neural networks, and closing the performance gap between neural networks learnt with and without unsupervised pre-training.

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.

Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence.

We explore an original strategy for building deep networks, based on stacking layers of denoising autoencoders which are trained locally to denoise corrupted versions of their inputs. The resulting algorithm is a straightforward variation on the stacking of ordinary autoencoders. It is however shown on a benchmark of classification problems to yield significantly lower classification error, thus bridging the performance gap with deep belief networks (DBN), and in several cases surpassing it. Higher level representations learnt in this purely unsupervised fashion also help boost the performance of subsequent SVM classifiers. Qualitative experiments show that, contrary to ordinary autoencoders, denoising autoencoders are able to learn Gabor-like edge detectors from natural image patches and larger stroke detectors from digit images. This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.

Complexity theory of circuits strongly suggests that deep architectures can be much more efficient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multi-layer neural networks have many levels of non-linearities allowing them to compactly represent highly non-linear and highly-varying functions. However, until recently it was not clear how to train such deep networks, since gradient-based optimization starting from random initialization appears to often get stuck in poor solutions. Hinton et al. recently introduced a greedy layer-wise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with many layers of hidden causal variables. In the context of the above optimization problem, we study this algorithm empirically and explore variants to better understand its success and extend it to cases where the inputs are continuous or where the structure of the input distribution is not revealing enough about the variable to be predicted in a supervised task. Our experiments also confirm the hypothesis that the greedy layer-wise unsupervised training strategy mostly helps the optimization, by initializing weights in a region near a good local minimum, giving rise to internal distributed representations that are high-level abstractions of the input, bringing better generalization.

Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of autoencoder variants with impressive results being obtained in several areas, mostly on vision and language datasets. The best results obtained on supervised learning tasks often involve an unsupervised learning component, usually in an unsupervised pre-training phase. The main question investigated here is the following: why does unsupervised pre-training work so well? Through extensive experimentation, we explore several possible explanations discussed in the literature including its action as a regularizer (Erhan et al., 2009b) and as an aid to optimization . Our results build on the work of Erhan et al. (2009b), showing that unsupervised pre-training appears to play predominantly a regularization role in subsequent supervised training. However our results in an online setting, with a virtually unlimited data stream, point to a somewhat more nuanced interpretation of the roles of optimization and regularization in the unsupervised pre-training effect.

受生物神经元的启发，激活功能在许多现实世界中常用的任何人工神经网络的学习过程中起着重要作用。文献中已经提出了各种激活功能，用于分类和回归任务。在这项工作中，我们调查了过去已经使用的激活功能以及当前的最新功能。特别是，我们介绍了多年来激活功能的各种发展以及这些激活功能的优势以及缺点或局限性。我们还讨论了经典（固定）激活功能，包括整流器单元和自适应激活功能。除了基于表征的激活函数的分类法外，还提出了基于应用的激活函数的分类法。为此，对MNIST，CIFAR-10和CIFAR-100等分类数据集进行了各种固定和自适应激活函数的系统比较。近年来，已经出现了一个具有物理信息的机器学习框架，以解决与科学计算有关的问题。为此，我们还讨论了在物理知识的机器学习框架中使用的激活功能的各种要求。此外，使用Tensorflow，Pytorch和Jax等各种机器学习库之间进行了不同的固定和自适应激活函数进行各种比较。

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。

近年来，神经网络已显示出巨大的增长，以解决许多问题。已经引入了各种类型的神经网络来处理不同类型的问题。但是，任何神经网络的主要目标是使用层层次结构将非线性可分离的输入数据转换为更线性可分离的抽象特征。这些层是线性和非线性函数的组合。最流行和常见的非线性层是激活功能（AFS），例如Logistic Sigmoid，Tanh，Relu，Elu，Swish和Mish。在本文中，在神经网络中为AFS提供了全面的概述和调查，以进行深度学习。涵盖了不同类别的AFS，例如Logistic Sigmoid和Tanh，基于RELU，基于ELU和基于学习的AFS。还指出了AFS的几种特征，例如输出范围，单调性和平滑度。在具有不同类型的数据的不同网络的18个最先进的AF中，还进行了性能比较。提出了AFS的见解，以使研究人员受益于进一步的研究和从业者在不同选择中进行选择。用于实验比较的代码发布于：\ url {https://github.com/shivram1987/activationfunctions}。

为了对线性不可分离的数据进行分类，神经元通常被组织成具有至少一个隐藏层的多层神经网络。灵感来自最近神经科学的发现，我们提出了一种新的神经元模型以及一种新的激活函数，可以使用单个神经元来学习非线性决策边界。我们表明标准神经元随后是新颖的顶端枝晶激活（ADA）可以使用100 \％的精度来学习XOR逻辑函数。此外，我们在计算机视觉，信号处理和自然语言处理中进行五个基准数据集进行实验，即摩洛哥，utkface，crema-d，时尚mnist和微小的想象成，表明ADA和泄漏的ADA功能提供了卓越的结果用于各种神经网络架构的整流线性单元（Relu），泄漏的Relu，RBF和嗖嗖声，例如单隐层或两个隐藏层的多层的Perceptrons（MLPS）和卷积神经网络（CNNS），如LENET，VGG，RESET和字符级CNN。当我们使用具有顶端树突激活（Pynada）的金字塔神经元改变神经元的标准模型时，我们获得进一步的性能改进。我们的代码可用于：https://github.com/raduionescu/pynada。

神经生成模型可用于学习从数据的复杂概率分布，从它们中进行采样，并产生概率密度估计。我们提出了一种用于开发由大脑预测处理理论启发的神经生成模型的计算框架。根据预测加工理论，大脑中的神经元形成一个层次结构，其中一个级别的神经元形成关于来自另一个层次的感觉输入的期望。这些神经元根据其期望与观察到的信号之间的差异更新其本地模型。以类似的方式，我们的生成模型中的人造神经元预测了邻近的神经元的作用，并根据预测匹配现实的程度来调整它们的参数。在这项工作中，我们表明，在我们的框架内学到的神经生成模型在练习中跨越多个基准数据集和度量来表现良好，并且保持竞争或显着优于具有类似功能的其他生成模型（例如变形自动编码器）。

神经网络经常将许多无关的概念包装到一个神经元中 - 一种令人困惑的现象被称为“多疾病”，这使解释性更具挑战性。本文提供了一个玩具模型，可以完全理解多义，这是由于模型在“叠加”中存储其他稀疏特征的结果。我们证明了相变的存在，与均匀多型的几何形状的令人惊讶的联系以及与对抗性例子联系的证据。我们还讨论了对机械解释性的潜在影响。

Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different "thinned" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.

An activation function has a significant impact on the efficiency and robustness of the neural networks. As an alternative, we evolved a cutting-edge non-monotonic activation function, Negative Stimulated Hybrid Activation Function (Nish). It acts as a Rectified Linear Unit (ReLU) function for the positive region and a sinus-sigmoidal function for the negative region. In other words, it incorporates a sigmoid and a sine function and gaining new dynamics over classical ReLU. We analyzed the consistency of the Nish for different combinations of essential networks and most common activation functions using on several most popular benchmarks. From the experimental results, we reported that the accuracy rates achieved by the Nish is slightly better than compared to the Mish in classification.

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-ofthe-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

尽管深度强化学习（RL）最近取得了许多成功，但其方法仍然效率低下，这使得在数据方面解决了昂贵的许多问题。我们的目标是通过利用未标记的数据中的丰富监督信号来进行学习状态表示，以解决这一问题。本文介绍了三种不同的表示算法，可以访问传统RL算法使用的数据源的不同子集使用：（i）GRICA受到独立组件分析（ICA）的启发，并训练深层神经网络以输出统计独立的独立特征。输入。 Grica通过最大程度地减少每个功能与其他功能之间的相互信息来做到这一点。此外，格里卡仅需要未分类的环境状态。 （ii）潜在表示预测（LARP）还需要更多的上下文：除了要求状态作为输入外，它还需要先前的状态和连接它们的动作。该方法通过预测当前状态和行动的环境的下一个状态来学习状态表示。预测器与图形搜索算法一起使用。 （iii）重新培训通过训练深层神经网络来学习国家表示，以学习奖励功能的平滑版本。该表示形式用于预处理输入到深度RL，而奖励预测指标用于奖励成型。此方法仅需要环境中的状态奖励对学习表示表示。我们发现，每种方法都有其优势和缺点，并从我们的实验中得出结论，包括无监督的代表性学习在RL解决问题的管道中可以加快学习的速度。

最近，稀疏的培训方法已开始作为事实上的人工神经网络的培训和推理效率的方法。然而，这种效率只是理论上。在实践中，每个人都使用二进制掩码来模拟稀疏性，因为典型的深度学习软件和硬件已针对密集的矩阵操作进行了优化。在本文中，我们采用正交方法，我们表明我们可以训练真正稀疏的神经网络以收获其全部潜力。为了实现这一目标，我们介绍了三个新颖的贡献，这些贡献是专门为稀疏神经网络设计的：（1）平行训练算法及其相应的稀疏实现，（2）具有不可训练的参数的激活功能，以支持梯度流动，以支持梯度流量， （3）隐藏的神经元对消除冗余的重要性指标。总而言之，我们能够打破记录并训练有史以来最大的神经网络在代表力方面训练 - 达到蝙蝠大脑的大小。结果表明，我们的方法具有最先进的表现，同时为环保人工智能时代开辟了道路。

我们引入了一种新型的数学公式，用于训练以（可能非平滑）近端图作为激活函数的馈送前向神经网络的培训。该公式基于布雷格曼的距离，关键优势是其相对于网络参数的部分导数不需要计算网络激活函数的导数。我们没有使用一阶优化方法和后传播的组合估算参数（如最先进的），而是建议使用非平滑一阶优化方法来利用特定结构新颖的表述。我们提出了几个数值结果，这些结果表明，与更常规的培训框架相比，这些训练方法可以很好地很好地适合于培训基于神经网络的分类器和具有稀疏编码的（DeNoising）自动编码器。

Sparse neural networks attract increasing interest as they exhibit comparable performance to their dense counterparts while being computationally efficient. Pruning the dense neural networks is among the most widely used methods to obtain a sparse neural network. Driven by the high training cost of such methods that can be unaffordable for a low-resource device, training sparse neural networks sparsely from scratch has recently gained attention. However, existing sparse training algorithms suffer from various issues, including poor performance in high sparsity scenarios, computing dense gradient information during training, or pure random topology search. In this paper, inspired by the evolution of the biological brain and the Hebbian learning theory, we present a new sparse training approach that evolves sparse neural networks according to the behavior of neurons in the network. Concretely, by exploiting the cosine similarity metric to measure the importance of the connections, our proposed method, Cosine similarity-based and Random Topology Exploration (CTRE), evolves the topology of sparse neural networks by adding the most important connections to the network without calculating dense gradient in the backward. We carried out different experiments on eight datasets, including tabular, image, and text datasets, and demonstrate that our proposed method outperforms several state-of-the-art sparse training algorithms in extremely sparse neural networks by a large gap. The implementation code is available on https://github.com/zahraatashgahi/CTRE

AI的一个关键挑战是构建体现的系统，该系统在动态变化的环境中运行。此类系统必须适应更改任务上下文并持续学习。虽然标准的深度学习系统实现了最先进的静态基准的结果，但它们通常在动态方案中挣扎。在这些设置中，来自多个上下文的错误信号可能会彼此干扰，最终导致称为灾难性遗忘的现象。在本文中，我们将生物学启发的架构调查为对这些问题的解决方案。具体而言，我们表明树突和局部抑制系统的生物物理特性使网络能够以特定于上下文的方式动态限制和路由信息。我们的主要贡献如下。首先，我们提出了一种新颖的人工神经网络架构，该架构将活跃的枝形和稀疏表示融入了标准的深度学习框架中。接下来，我们在需要任务的适应性的两个单独的基准上研究这种架构的性能：Meta-World，一个机器人代理必须学习同时解决各种操纵任务的多任务强化学习环境;和一个持续的学习基准，其中模型的预测任务在整个训练中都会发生变化。对两个基准的分析演示了重叠但不同和稀疏的子网的出现，允许系统流动地使用最小的遗忘。我们的神经实现标志在单一架构上第一次在多任务和持续学习设置上取得了竞争力。我们的研究揭示了神经元的生物学特性如何通知深度学习系统，以解决通常不可能对传统ANN来解决的动态情景。