Working memory requires information about external stimuli to be represented in the brain even after those stimuli go away. This information is encoded in the activities of neurons, and neural activities change over timescales of tens of milliseconds. Information in working memory, however, is retained for tens of seconds, suggesting the question of how time-varying neural activities maintain stable representations. Prior work shows that, if the neural dynamics are in the `  null space' of the representation - so that changes to neural activity do not affect the downstream read-out of stimulus information - then information can be retained for periods much longer than the time-scale of individual-neuronal activities. The prior work, however, requires precisely constructed synaptic connectivity matrices, without explaining how this would arise in a biological neural network. To identify mechanisms through which biological networks can self-organize to learn  memory function, we derived biologically plausible synaptic plasticity rules that dynamically modify the connectivity matrix to enable information storing. Networks implementing this plasticity rule can successfully learn to form memory representations even if only 10% of the synapses are plastic, they are robust to synaptic noise, and they can represent information about multiple stimuli.

我们推导出了生物学上合理的突触可塑性学习规则，用于存储刺激表征的循环神经网络。

一个由循环连接的神经元和一个或多个再输出组成的神经网络模型，其目的是在一段时间内保留一些输出。

本文提出了一个神经模型中的自组织记忆机制，并引入了一个目标函数，使要记忆的信号的变化最小。


Generative Adversarial Networks (GANs) have been proposed as an approach to learning generative models. While GANs have demonstrated promising performance on multiple vision tasks, their learning dynamics are not yet well understood, neither in theory nor in practice. In particular, the work in this domain has been focused so far only on understanding the properties of the stationary solutions that this dynamics might converge to, and of the behavior of that dynamics in this solutions’ immediate neighborhood.   To address this issue, in this work we take a first step towards a principled study of the GAN dynamics itself. To this end, we propose a model that, on one hand, exhibits several of the common problematic convergence behaviors (e.g., vanishing gradient, mode collapse, diverging or oscillatory behavior), but on the other hand, is sufficiently simple to enable rigorous convergence analysis.   This methodology enables us to exhibit an interesting phenomena: a GAN with an optimal discriminator provably converges, while guiding the GAN training using only a first order approximation of the discriminator leads to unstable GAN dynamics and mode collapse. This suggests that such usage of the first order approximation of the discriminator, which is a de-facto standard in all the existing GAN dynamics, might be one of the factors that makes GAN training so challenging in practice. Additionally, our convergence result constitutes the first rigorous analysis of a dynamics of a concrete parametric GAN.

为了理解GAN训练，我们定义了简单的GAN动力学，并展示了该模型中最优和一阶更新之间的定量差异。

作者研究了GAN在以下情况下的影响：在每个迭代中，判别器训练到收敛，生成器用梯度步骤更新，或者对判别器和生成器做几个梯度步骤。

本文研究了高斯混合模型上GAN的对抗性训练的动态情况


The machine learning and computer vision community is witnessing an unprecedented rate of new tasks being proposed and addressed, thanks to the power of deep convolutional networks to find complex mappings from X to Y. The advent of each task often accompanies the release of a large-scale human-labeled dataset, for supervised training of the deep network. However, it is expensive and time-consuming to manually label sufficient amount of training data. Therefore, it is important to develop algorithms that can leverage off-the-shelf labeled dataset to learn useful knowledge for the target task. While previous works mostly focus on transfer learning from a single source, we study multi-source transfer across domains and tasks (MS-DTT), in a semi-supervised setting. We propose GradMix, a model-agnostic method applicable to any model trained with gradient-based learning rule. GradMix transfers knowledge via gradient descent, by weighting and mixing the gradients from all sources during training. Our method follows a meta-learning objective, by assigning layer-wise weights to the source gradients, such that the combined gradient follows the direction that can minimize the loss for a small set of samples from the target dataset. In addition, we propose to adaptively adjust the learning rate for each mini-batch based on its importance to the target task, and a pseudo-labeling method to leverage the unlabeled samples in the target domain. We perform experiments on two MS-DTT tasks: digit recognition and action recognition, and demonstrate the advantageous performance of the proposed method against multiple baselines.

我们提出了一种基于梯度的方法，在不同的领域和任务中从多个来源转移知识。 本文提出结合源域的梯度来帮助目标域的学习。


Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo with simple mechanisms for proposing new states, which hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates. In this paper we present an alternative approach: a variational framework for Bayesian phylogenetic analysis. We approximate the true posterior using an expressive graphical model for tree distributions, called a subsplit Bayesian network, together with appropriate branch length distributions. We train the variational approximation via stochastic gradient ascent and adopt multi-sample based gradient estimators for different latent variables separately to handle the composite latent space of phylogenetic models. We show that our structured variational approximations are flexible enough to provide comparable posterior estimation to MCMC, while requiring less computation due to a more efficient tree exploration mechanism enabled by variational inference. Moreover, the variational approximations can be readily used for further statistical analysis such as marginal likelihood estimation for model comparison via importance sampling. Experiments on both synthetic data and real data Bayesian phylogenetic inference problems demonstrate the effectiveness and efficiency of our methods.

系统发育推理的第一个变分贝叶斯公式，这是一个具有挑战性的推理问题，涉及离散和连续成分交织的结构。

探讨了利用最近提出的子分裂贝叶斯网络和现代梯度估计器对系统发育树的贝叶斯推断问题的近似推断方案。

提出了一种系统发育树的贝叶斯后验推断的变分方法。


This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive  models for raw audio waveform generation. As an example, we propose  a hybrid model that combines an autoregressive network named WaveNet and a  conventional LSTM model to address speech synthesis. Instead of generating  one sample per time-step, the proposed HybridNet generates multiple samples per  time-step by exploiting the long-term memory utilization property of LSTMs. In  the evaluation, when applied to text-to-speech, HybridNet yields state-of-art performance.  HybridNet achieves a 3.83 subjective 5-scale mean opinion score on  US English, largely outperforming the same size WaveNet in terms of naturalness  and provide 2x speed up at inference.

它是一个混合神经架构，以加速自回归模型。

结论是，为了在不增加推理时间的情况下扩大模型规模，进行顺序预测，要使用一次预测多个时间步长的模型。

本文介绍了HybridNet，一个神经语音和其他音频合成系统，它将WaveNet模型与LSTM结合起来，目的是提供一个具有更快的推理时间的音频生成模型。


Visual Interpretation and explanation of deep models is critical towards wide adoption of systems that rely on them. In this paper, we propose a novel scheme for both interpretation as well as explanation in which, given a pretrained model, we automatically identify internal features relevant for the set of classes considered by the model, without relying on additional annotations. We interpret the model through average visualizations of this reduced set of features. Then, at test time, we explain the network prediction by accompanying the predicted class label with supporting visualizations derived from the identified features. In addition, we propose a method to address the artifacts introduced by strided operations in deconvNet-based visualizations. Moreover, we introduce an8Flower , a dataset specifically designed for objective quantitative evaluation of methods for visual explanation. Experiments on the MNIST , ILSVRC 12, Fashion 144k and an8Flower datasets show that our method produces detailed explanations with good coverage of relevant features of the classes of interest.

通过识别作为感兴趣的任务的指标的模型学习的特征进行解释。通过强调这些特征在测试数据中的反应来解释模型决策。用一个受控的数据集客观地评估解释。

本文提出了一种为深度神经网络输出制作视觉解释的方法，并发布了一个新的合成数据集。

一种用于深层神经网络的方法，可以自动识别类集的相关特征，支持解释和说明，而不依赖额外的注释。


In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data. Drawing inspiration from the distributional hypothesis and recent work on learning sentence representations, we reformulate the problem of predicting the context in which a sentence appears as a classification problem. Given a sentence and the context in which it appears, a classifier distinguishes context sentences from other contrastive sentences based on their vector representations. This allows us to efficiently learn different types of encoding functions, and we show that the model learns high-quality sentence representations. We demonstrate that our sentence representations outperform state-of-the-art unsupervised and supervised representation learning methods on several downstream NLP tasks that involve understanding sentence semantics while achieving an order of magnitude speedup in training time.

一个高效学习高质量句子表征的框架。

提出了一种更快的算法，用于从有序句子的语料库中学习SkipThought式的句子表征，将词级解码器换成对比性分类损失。

本文提出了一个无监督学习句子表征的框架，通过最大化真实上下文句子相对于随机候选句子的概率模型来实现


Many regularization methods have been proposed to prevent overfitting in neural networks. Recently, a regularization method has been proposed to optimize the variational lower bound of the Information Bottleneck Lagrangian. However, this method cannot be generalized to regular neural network architectures. We present the activation norm penalty that is derived from the information bottleneck principle and is theoretically grounded in a variation dropout framework. Unlike in previous literature, it can be applied to any general neural network. We demonstrate that this penalty can give consistent improvements to different state of the art architectures both in language modeling and image classification. We present analyses on the properties of this penalty and compare it to other methods that also reduce mutual information.

我们从信息瓶颈的角度推导出对神经网络输出的范数惩罚 提出了激活范数惩罚，这是一种对激活的L_2类型的正则化，源于信息瓶颈原则 本文利用变分丢弃框架在激活范数惩罚和信息瓶颈框架之间建立了一个映射。


Unsupervised learning of timeseries data is a challenging problem in machine learning. Here,  we propose a novel algorithm, Deep Temporal Clustering (DTC), a fully unsupervised method, to naturally integrate dimensionality reduction and temporal clustering into a single end to end learning framework. The algorithm starts with an initial cluster estimates using an autoencoder for dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objective. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics are considered and compared.   To gain insight into features that the network has learned for its clustering, we apply a visualization method that generates a heat map of regions of interest in the timeseries. The viability of the algorithm is demonstrated using timeseries data from diverse domains, ranging from earthquakes to sensor data from spacecraft. In each case, we show that our algorithm outperforms traditional methods. This performance is attributed to fully integrated temporal dimensionality reduction and clustering criterion.

一个完全无监督的方法，将降维和时间聚类自然地整合到一个单一的端到端学习框架中。

提出了一种算法，利用适合时间序列数据的网络结构，将自动编码器与时间序列数据聚类结合起来。

一种在深度学习背景下联合进行降维和时间聚类的算法，利用了自动编码器和聚类目标。

作者提出了一种用深度神经网络构建的无监督时间序列聚类方法，并配备了编码器-解码器和聚类模式，以缩短时间序列，提取局部时间特征，并获得编码表征。


We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning scenarios. Compared to the well-studied many-class many-shot and few-class few-shot problems, MCFS problem commonly occurs in practical applications but is rarely studied. MCFS brings new challenges because it needs to distinguish between many classes, but only a few samples per class are available for training. In this paper, we propose ``memory-augmented hierarchical-classification network (MahiNet)'' for MCFS learning. It addresses the ``many-class'' problem by exploring the class hierarchy, e.g., the coarse-class label that covers a subset of fine classes, which helps to narrow down the candidates for the fine class and is cheaper to obtain. MahiNet uses a convolutional neural network (CNN) to extract features, and integrates a memory-augmented attention module with a multi-layer perceptron (MLP) to produce the probabilities over coarse and fine classes. While the MLP extends the linear classifier, the attention module extends a KNN classifier, both together targeting the ''`few-shot'' problem. We design different training strategies of MahiNet for supervised learning and meta-learning. Moreover, we propose two novel benchmark datasets ''mcfsImageNet'' (as a subset of ImageNet) and ''mcfsOmniglot'' (re-splitted Omniglot) specifically for MCFS problem. In experiments, we show that MahiNet outperforms several state-of-the-art models on MCFS classification tasks in both supervised learning and meta-learning scenarios.

一个记忆增强的神经网络，通过利用监督学习和元学习中的类层次，解决了多类少样本的问题。

本文介绍了通过沿阶级层次从粗到细的预测和学习基于记忆的KNN分类器，在学习过程中跟踪错误标记的实例，向分类器添加归纳偏见的方法。

本文从监督学习的角度和元学习的角度阐述了多类少样本分类问题。


Learning a better representation with neural networks is a challenging problem, which has been tackled from different perspectives in the past few years. In this work, we focus on learning a representation that would be useful in a clustering task. We introduce two novel loss components that substantially improve the quality of produced clusters, are simple to apply to arbitrary models and cost functions, and do not require a complicated training procedure. We perform an extensive set of experiments, supervised and unsupervised, and evaluate the proposed loss components on two most common types of models, Recurrent Neural Networks and Convolutional Neural Networks, showing that the approach we propose consistently improves the quality of KMeans clustering in terms of mutual information scores and outperforms previously proposed methods.

一种新颖的损失成分，迫使网络在分类任务的训练中学习一种非常适合聚类的表征。

本文提出了两个基于两个softmax归一化输入参数之间的KL发散的复合铰链损失的正则化项，以鼓励学习不相干的代表。

提出了两个正则器，旨在使分类器倒数第二层学到的表征更符合数据的固有结构。


In high dimensions, the performance of nearest neighbor algorithms depends crucially on structure in the data.  While traditional nearest neighbor datasets consisted mostly of hand-crafted feature vectors, an increasing number of datasets comes from representations learned with neural networks.  We study the interaction between nearest neighbor algorithms and neural networks in more detail.  We find that the network architecture can significantly influence the efficacy of nearest neighbor algorithms even when the classification accuracy is unchanged.  Based on our experiments, we propose a number of training modifications that lead to significantly better datasets for nearest neighbor algorithms.  Our modifications lead to learned representations that can accelerate nearest neighbor queries by 5x.

我们展示了如何从Simiarity Search的角度获得良好的表示。

研究改变DNN上面的图像分类部分对用LSH或kd-tree算法来索引描述符的能力的影响。

建议使用 softmax 交叉熵损失来学习一个网络，该网络试图在监督框架中使用减少输入和相应类向量之间的角度。


Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks that can be effectively discretized without loss of performance, we introduce a differentiable quantization procedure. Differentiability can be achieved by transforming continuous distributions over the weights and activations of the network to categorical distributions over the quantization grid. These are subsequently relaxed to continuous surrogates that can allow for efficient gradient-based optimization. We further show that stochastic rounding can be seen as a special case of the proposed approach and that under this formulation the quantization grid itself can also be optimized with gradient descent. We experimentally validate the performance of our method on MNIST, CIFAR 10 and Imagenet classification.

我们引入了一种技术，可以对量化的神经网络进行基于梯度的训练。

提出了一种统一的、通用的训练神经网络的方法，即降低精度的量化突触权重和激活。

一种量化激活的新方法，在几个真实的图像问题上是最先进的或有竞争力的。

一种通过随机量化数值并以连续松弛取代所产生的分类分布来学习具有量化权重和激活的神经网络的方法


In most current formulations of adversarial training, the discriminators can be expressed as single-input operators, that is, the mapping they define is separable over observations. In this work, we argue that this property might help explain the infamous mode collapse phenomenon in adversarially-trained generative models. Inspired by discrepancy measures and two-sample tests between probability distributions, we propose distributional adversaries that operate on samples, i.e., on sets of multiple points drawn from a distribution, rather than on single observations. We show how they can be easily implemented on top of existing models. Various experimental results show that generators trained in combination with our distributional adversaries are much more stable and are remarkably less prone to mode collapse than traditional models trained with observation-wise prediction discriminators. In addition, the application of our framework to domain adaptation results in strong improvement over recent state-of-the-art.

我们表明，GAN中的模式崩溃问题可能是由训练批次中的观察值之间缺乏信息共享来解释的，并提出了一个基于分布的梯度间信息全局共享框架，导致更稳定和有效的对抗性训练。

建议在对抗性训练中用明确操作实例分布的判别器取代单样本判别器。

关于双样本检验和MMD的理论，以及如何将其有益地纳入GAN框架。


Chemical information extraction is to convert chemical knowledge in text into true chemical database, which is a text processing task heavily relying on chemical compound name identification and standardization. Once a systematic name for a chemical compound is given, it will naturally and much simply convert the name into the eventually required molecular formula. However, for many chemical substances, they have been shown in many other names besides their systematic names which poses a great challenge for this task. In this paper, we propose a framework to do the auto standardization from the non-systematic names to the corresponding systematic names by using the spelling error correction, byte pair encoding tokenization and neural sequence to sequence model. Our framework is trained end to end and is fully data-driven. Our standardization accuracy on the test dataset achieves 54.04% which has a great improvement compared to previous state-of-the-art result.

我们设计了一个端到端的框架，使用序列到序列的模型来进行化学名称的标准化。

通过建立非系统性名称和系统性名称的平行语料库并建立seq2seq模型，使化学信息提取中的非系统性名称标准化。

这项工作提出了一种方法，利用各种机制的组合将化合物的非系统名称转化成其系统等价物


The training of deep neural networks with Stochastic Gradient Descent (SGD) with a large learning rate or a small batch-size typically ends in flat regions of the weight space, as indicated by small eigenvalues of the Hessian of the training loss. This was found to correlate with a good final generalization performance.   In this paper we extend previous work by investigating the curvature of the loss surface along the whole training trajectory, rather than only at the endpoint. We find that initially SGD visits increasingly sharp regions, reaching a maximum sharpness determined by both the learning rate and the batch-size of SGD. At this peak value SGD starts to fail to minimize the loss along directions in the loss surface corresponding to the largest curvature (sharpest directions). To further investigate the effect of these dynamics in the training process, we study a variant of SGD using a reduced learning rate along the sharpest directions which we show can improve training speed while finding both sharper and better generalizing solution, compared to vanilla SGD. Overall, our results show that the SGD dynamics in the subspace of the sharpest directions influence the regions that SGD steers to (where larger learning rate or smaller batch size result in wider regions visited), the overall training speed, and the generalization ability of the final model.

在训练的早期，SGD被引导到一个区域，在这个区域里，它的步长与曲率相比太大，这影响了训练的其余部分。

分析了收敛/泛化与DNN经验损失的Hessian的最大特征向量的更新之间的关系。

这项工作研究了SGD步长和损失表面的曲率之间的关系


We introduce a new approach to estimate continuous actions using actor-critic algorithms for reinforcement learning problems. Policy gradient methods usually predict one continuous action estimate or parameters of a presumed distribution (most commonly Gaussian) for any given state which might not be optimal as it may not capture the complete description of the target distribution. Our approach instead predicts M actions with the policy network (actor) and then uniformly sample one action during training as well as testing at each state. This allows the agent to learn a simple stochastic policy that has an easy to compute expected return. In all experiments, this facilitates better exploration of the state space during training and converges to a better policy.

我们引入了一种新的强化学习算法，该算法可以预测多个行动并从中取样。

这项工作引入了确定性策略的统一混合物，并发现这种随机政策的参数化在几个OpenAI体育馆的基准上优于DDPG。

作者研究了一种改善用DDPG训练的网络性能的方法，并显示了在大量标准连续控制环境下的性能改善。


Recently convolutional neural networks (CNNs) achieve great accuracy in visual recognition tasks. DenseNet becomes one of the most popular CNN models due to its effectiveness in feature-reuse. However, like other CNN models, DenseNets also face overfitting problem if not severer. Existing dropout method can be applied but not as effective due to the introduced nonlinear connections. In particular, the property of feature-reuse in DenseNet will be impeded, and the dropout effect will be weakened by the spatial correlation inside feature maps. To address these problems, we craft the design of a specialized dropout method from three aspects, dropout location, dropout granularity, and dropout probability. The insights attained here could potentially be applied as a general approach for boosting the accuracy of other CNN models with similar nonlinear connections. Experimental results show that DenseNets with our specialized dropout method yield better accuracy compared to vanilla DenseNet and state-of-the-art CNN models, and such accuracy boost increases with the model depth.

意识到在DenseNet上应用原始丢弃时的缺点，我们从三个方面精心设计了丢弃方法，其理念也可以应用于其他CNN模型。

应用不同的二元丢弃结构和时间表，具体目的是使DenseNet结构正规化。

提出了一种用于densenet的预丢弃技术，该技术在非线性激活函数之前实现了丢弃。


While extremely successful in several applications, especially with low-level representations; sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models. Column Networks, a deep architecture, can succinctly capture such domain structure and interactions, but may still be prone to sub-optimal learning from sparse and noisy samples. Inspired by the success of human-advice guided learning in AI, especially in data-scarce domains, we propose Knowledge-augmented Column Networks that leverage human advice/knowledge for better learning with noisy/sparse samples. Our experiments demonstrate how our approach leads to either superior overall performance or faster convergence.

引导关系感知的深度模型更好地学习人类知识。 这项工作提出了一种基于注入人类指导的柱状网络的变体，通过修改网络中的计算结果。 通过扩展列网络（一种用于集体分类的图形神经网络）将人类建议纳入深度学习的方法。


Recent research has shown that one can train a neural network with binary weights and activations at train time by augmenting the weights with a high-precision continuous latent variable that accumulates small changes from stochastic gradient descent. However, there is a dearth of work to explain why one can effectively capture the features in data with binary weights and activations. Our main result is that the neural networks with binary weights and activations trained using the method of Courbariaux, Hubara et al. (2016) work because of the high-dimensional geometry of binary vectors. In particular, the ideal continuous vectors that extract out features in the intermediate representations of these BNNs are well-approximated by binary vectors in the sense that dot products are approximately preserved. Compared to previous research that demonstrated good classification performance with BNNs, our work explains why these BNNs work in terms of HD geometry.   Furthermore, the results and analysis used on BNNs are shown to generalize to neural networks with ternary weights and activations. Our theory serves as a foundation for understanding not only BNNs but a variety of methods that seek to compress traditional neural networks. Furthermore, a better understanding of multilayer binary neural networks serves as a starting point for generalizing BNNs to other neural network architectures such as recurrent neural networks.

最近二元神经网络的成功可以基于高维二元向量的几何学来理解 从数字和理论上研究了二值化神经网络的经验成功背后的原因。 本文分析了二元神经网络的有效性，以及为什么二元化能够保持模型性能。


In recent years Convolutional Neural Networks (CNN) have been used extensively for Superresolution (SR). In this paper, we use inverse problem and sparse representation solutions to form a mathematical basis for CNN operations. We show how a single neuron is able to provide the optimum solution for inverse problem, given a low resolution image dictionary as an operator. Introducing a new concept called Representation Dictionary Duality, we show that CNN elements (filters) are trained to be representation vectors and then, during reconstruction, used as dictionaries. In the light of theoretical work, we propose a new algorithm which uses two networks with different structures that are separately trained with low and high coherency image patches and show that it performs faster compared to the state-of-the-art algorithms while not sacrificing from performance.

在证明了一个神经元作为超分辨率的逆向问题解决者和一个神经元网络保证提供解决方案后，我们提出了一个双网络架构，其性能比最先进的要快。

讨论了使用神经网络进行超分辨率的问题

一个用于解决图像超分辨率任务的新架构，以及旨在建立用于解决超分辨率的CNN与解决稀疏正则化反问题之间联系的分析。


We consider the learning of algorithmic tasks by mere observation of input-output  pairs. Rather than studying this as a black-box discrete regression problem with  no assumption whatsoever on the input-output mapping, we concentrate on tasks  that are amenable to the principle of divide and conquer, and study what are its  implications in terms of learning.  This principle creates a powerful inductive bias that we leverage with neural  architectures that are defined recursively and dynamically, by learning two scale-  invariant atomic operations: how to split a given input into smaller sets, and how  to merge two partially solved tasks into a larger partial solution. Our model can be  trained in weakly supervised environments, namely by just observing input-output  pairs, and in even weaker environments, using a non-differentiable reward signal.  Moreover, thanks to the dynamic aspect of our architecture, we can incorporate  the computational complexity as a regularization term that can be optimized by  backpropagation. We demonstrate the flexibility and efficiency of the Divide-  and-Conquer Network on several combinatorial and geometric tasks: convex hull,  clustering, knapsack and euclidean TSP. Thanks to the dynamic programming  nature of our model, we show significant improvements in terms of generalization  error and computational complexity.

通过弱监督来学习分而治之策略的动态模型。

提出通过使用分而治之的策略为神经网络架构增加新的归纳偏压。

本文研究了可以用动态编程方法解决的问题，并提出了一种神经网络架构来解决这种问题，这种架构击败了序列的基线。

本文提出了一个独特的网络架构，可以学习分而治之的策略来解决算法任务。


Within many machine learning algorithms, a fundamental problem concerns efficient calculation of an unbiased gradient wrt parameters $\boldsymbol{\gamma}$ for expectation-based objectives $\mathbb{E}_{q_{\boldsymbol{\gamma}} (\boldsymbol{y})} [f (\boldsymbol{y}) ]$. Most existing methods either ($i$) suffer from high variance, seeking help from (often) complicated variance-reduction techniques; or ($ii$) they only apply to reparameterizable continuous random variables and employ a reparameterization trick. To address these limitations, we propose a General and One-sample (GO) gradient that ($i$) applies to many distributions associated with non-reparameterizable continuous {\em or} discrete random variables, and ($ii$) has the same low-variance as the reparameterization trick. We find that the GO gradient often works well in practice based on only one Monte Carlo sample (although one can of course use more samples if desired). Alongside the GO gradient, we develop a means of propagating the chain rule through distributions, yielding statistical back-propagation, coupling neural networks to common random variables.

对于不可重新参数化的连续/离散分布的类似Rep的梯度；进一步推广到深度概率模型，产生统计反传播

提出了一个基于期望目标的梯度估计器，它是无偏的，具有低方差，并适用于连续和离散的随机变量。

一种改进的计算期望值导数的方法，以及一种新的低方差梯度估计器，允许在观测或潜变量是离散的情况下训练生成模型。

为与连续或离散随机变量相关的分布设计一个低方差梯度。


Quantum computers promise significant advantages over classical computers for a number of different applications. We show that the complete loss function landscape of a neural network can be represented as the quantum state output by a quantum computer. We demonstrate this explicitly for a binary neural network and, further, show how a quantum computer can train the network by manipulating this state using a well-known algorithm known as quantum amplitude amplification. We further show that with minor adaptation, this method can also represent the meta-loss landscape of a number of neural network architectures simultaneously. We search this meta-loss landscape with the same method to simultaneously train and design a binary neural network.

我们表明，NN参数和超参数成本景观可以用一个单一的量子电路作为量子态生成，这些可以用于训练和元训练。

描述了一种方法，即深度学习框架可以通过考虑布洛赫球体/量子比特的两种状态形式来进行量化，并创建一个量子二元神经网络。

本文提出了量子振幅放大法，这是一种用于二元神经网络中训练和模型选择的新算法。

提出了一个新颖的想法，即通过构建量子二元神经网络（QBNN），输出代表一个给定二元神经网络所有参数的完整成本景观的量子状态。


Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. These methods assume that all the adversarial transformations are equally important, which is seldom the case in real-world applications. We advocate for cost-sensitive robustness as the criteria for measuring the classifier's performance for tasks where some adversarial transformation are more important than others. We encode the potential harm of each adversarial transformation in a cost matrix, and propose a general objective function to adapt the robust training method of Wong & Kolter (2018) to optimize for cost-sensitive robustness. Our experiments on simple MNIST and CIFAR10 models with a variety of cost matrices show that the proposed approach can produce models with substantially reduced cost-sensitive robust error, while maintaining classification accuracy.

用于训练经过认证的成本敏感的稳健分类器的一般方法，以对抗对抗性扰动

计算并将对抗性攻击的成本插入优化的目标中，得到一个对对抗性攻击具有成本敏感性的模型。

在Dalvi等人的半年度工作的基础上，扩展了可认证稳健性的方法，用一个成本矩阵来指定每一对源-目标类别的模型是否应该对对抗性例子具有稳健性。


Retinal prostheses for treating incurable blindness are designed to electrically stimulate surviving retinal neurons,  causing them to send artificial visual signals to the brain. However, electrical stimulation generally cannot precisely reproduce  normal patterns of neural activity in the retina. Therefore, an electrical stimulus must be selected that produces a neural response as close as possible to the desired response. This requires a technique for computing a distance between the desired response and the achievable response that is meaningful in terms of the visual signal being conveyed. Here we propose a method to learn such a metric on neural responses, directly from recorded light responses of a population of retinal ganglion cells (RGCs) in the primate retina. The learned metric produces a measure of similarity of RGC population responses that accurately reflects the similarity of the visual input. Using data from electrical stimulation experiments, we demonstrate that this metric may improve the performance of a prosthesis.

使用三联体来学习比较神经反应的指标并提高假体的性能。

作者开发了新的尖峰训练距离度量，包括神经网络和二次元度量。这些指标被证明优于天真的汉明距离指标，并隐含地捕捉了神经代码中的一些结构。

考虑到改进神经假体的应用，作者提议通过优化二次方形式或深度神经网络来学习神经反应之间的度量。


We introduce a novel workflow, QCue, for providing textual stimulation during mind-mapping. Mind-mapping is a powerful tool whose intent is to allow one to externalize ideas and their relationships surrounding a central problem. The key challenge in mind-mapping is the difficulty in balancing the exploration of different aspects of the problem (breadth) with a detailed exploration of each of those aspects (depth). Our idea behind QCue is based on two mechanisms: (1) computer-generated automatic cues to stimulate the user to explore the breadth of topics based on the temporal and topological evolution of a mind-map and (2) user-elicited queries for helping the user explore the depth for a given topic. We present a two-phase study wherein the first phase provided insights that led to the development of our work-flow for stimulating the user through cues and queries. In the second phase, we present a between-subjects evaluation comparing QCue with a digital mind-mapping work-flow without computer intervention. Finally, we present an expert rater evaluation of the mind-maps created by users in conjunction with user feedback.

本文介绍了一种生成问题（线索）和查询（建议）以帮助用户进行思维导图的方法。

提出了一个工具，通过与现有节点相关的建议背景，以及通过对不太发达的分支进行扩展的问题，来帮助思维导图。

本文提出了一种帮助人们完成思维导图任务的方法，设计了一种界面和算法特征来支持思维导图，并提供了一项评估性研究。


The ability to detect when an input sample was not drawn from the training distribution is an important  desirable property of deep neural networks. In this paper, we show that a simple ensembling of first and second order deep feature statistics can be exploited to effectively differentiate in-distribution and out-of-distribution samples. Specifically, we observe that  the mean and standard deviation within feature maps  differs greatly between in-distribution and out-of-distribution samples. Based on this observation, we propose a simple and  efficient plug-and-play detection procedure that does not require re-training, pre-processing or changes to the model.   The proposed method outperforms the state-of-the-art by a large margin in all standard benchmarking tasks, while being much simpler to implement and execute. Notably, our method improves the true negative rate from 39.6% to 95.3% when 95% of in-distribution (CIFAR-100) are correctly detected using a DenseNet and the out-of-distribution dataset is TinyImageNet resize. The source code of our method will be made publicly available.

在不需要改变底层DNN的情况下，通过使用低阶特征统计来检测分布外的样本。

提出了一种算法，通过使用BatchNorm层内的均值和方差的运行估计来检测分布外的样本，以构建后来被送入线性分类器的特征表示。

一种检测分布外样本的方法，作者提出在CNN的每个批次规范化层的简单统计上使用逻辑回归。

该论文建议使用Z-cores来比较ID和OOD样本，以评估深层网络所要做的事情。


Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security. However, most of the work in binary code vulnerability detection has relied on handcrafted features which are manually chosen by a select few, knowledgeable domain experts. In this paper, we attempt to alleviate this severe binary vulnerability detection bottleneck by leveraging recent advances in deep learning representations and propose the Maximal Divergence Sequential Auto-Encoder. In particular, latent codes representing vulnerable and non-vulnerable binaries are encouraged to be maximally divergent, while still being able to maintain crucial information from the original binaries. We conducted extensive experiments to compare and contrast our proposed methods with the baselines, and the results show that our proposed methods outperform the baselines in all performance measures of interest.

我们提出了一种名为 "最大发散顺序自动编码器 "的新方法，该方法利用变分自动编码器的表示方法进行二进制代码漏洞检测。

本文提出了一个基于变分自动编码器的架构，用于二进制软件漏洞检测的代码嵌入，与基线相比，学到的嵌入在区分易受攻击和非不易受攻击二进制代码方面更加有效。

本文提出了一个模型，利用深度学习技术自动提取漏洞检测的特征。


Modern neural architectures critically rely on attention for mapping structured inputs to sequences. In this paper we show that prevalent attention architectures do not adequately model the dependence among the attention and output tokens across a predicted sequence.  We present an alternative architecture called  Posterior Attention Models that after a principled factorization of the full joint distribution of the attention and output variables, proposes two major changes.   First, the position where attention is marginalized is changed from the input to the output. Second, the attention propagated to the next decoding stage is a posterior attention distribution conditioned on the output. Empirically on five translation and two morphological inflection tasks the proposed posterior attention models yield better BLEU score and alignment accuracy than existing attention models.

基于后验分布的注意力计算会导致更有意义的注意力和更好的表现

本文提出了一个序列到序列的模型，其中注意力被视为一个潜在的变量，并为这个模型推导出新颖的推理程序，在机器翻译和语态转折生成任务中获得改进。

本文提出了一种新型的seq2seq问题的后发注意力模型


The growing interest to implement Deep Neural Networks (DNNs) on resource-bound hardware has motivated innovation of compression algorithms. Using these algorithms, DNN model sizes can be substantially reduced, with little to no accuracy degradation. This is achieved by either eliminating components from the model, or penalizing complexity during training. While both approaches demonstrate considerable compressions, the former often ignores the loss function during compression while the later produces unpredictable compressions. In this paper, we propose a technique that directly minimizes both the model complexity and the changes in the loss function. In this technique, we formulate compression as a constrained optimization problem, and then present a solution for it. We will show that using this technique, we can achieve competitive results.

通过最小化其复杂性，同时约束其损失来压缩训练的DNN模型。

本文提出了一种在精度约束下的深度神经网络压缩方法。

本文提出了一种用于网络压缩的损失值约束的k-means编码方法，并开发了一种模型优化的迭代算法。


Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned mechanisms that contribute to such effective behaviors and more critically, failure modes. In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations. Our dataset-centric method produces visualizations of how a trained network attends to components of its inputs. The computed "attention masks" support improved interpretability by highlighting which input attributes are critical in determining output. We demonstrate the effectiveness of our framework on a variety of deep neural network architectures in domains from computer vision and natural language processing. The primary contribution of our approach is an interpretable visualization of attention that provides unique insights into the network's underlying decision-making process irrespective of the data modality.

我们开发了一种技术来可视化任意神经网络中的注意力机制。 提出学习一个潜在注意力网络，可以帮助可视化深度神经网络的内部结构。 本文作者提出了一个数据驱动的黑箱可视化方案。


The design of small molecules with bespoke properties is of central importance to drug discovery.   However significant challenges yet remain for computational methods, despite recent advances such as deep recurrent networks and reinforcement learning strategies for sequence generation, and it can be difficult to compare results across different works.   This work proposes 19 benchmarks selected by subject experts, expands smaller datasets previously used to approximately 1.1 million training molecules, and explores how to apply new reinforcement learning techniques effectively for molecular design.   The benchmarks here, built as OpenAI Gym environments, will be open-sourced to encourage innovation in molecular design algorithms and to enable usage by those without a background in chemistry.   Finally, this work explores recent development in reinforcement-learning methods with excellent sample complexity (the A2C and PPO algorithms) and investigates their behavior in molecular generation, demonstrating significant performance gains compared to standard reinforcement learning techniques.

我们研究了各种用于分子生成的RL算法，并定义了新的基准（将作为OpenAI Gym发布），发现PPO和一个爬坡MLE算法效果最好。

通过提出19个基准，将小型数据集扩展到大型标准化数据集，并探讨如何将RL技术应用于分子设计，考虑了分子生成的模型评估。

本文表明，在建模和合成分子时，最复杂的RL方法不如简单的爬坡技术有效，PPO是例外。


Analogical reasoning has been a principal focus of various waves of AI research. Analogy is particularly challenging for machines because it requires relational structures to be represented such that they can be flexibly applied across diverse domains of experience. Here, we study how analogical reasoning can be induced in neural networks that learn to perceive and reason about raw visual data. We find that the critical factor for inducing such a capacity is not an elaborate architecture, but rather, careful attention to the choice of data and the manner in which it is presented to the model. The most robust capacity for analogical reasoning is induced when networks learn analogies by contrasting abstract relational structures in their input domains, a training method that uses only the input data to force models to learn about important abstract features. Using this technique we demonstrate capacities for complex, visual and symbolic analogy making and generalisation in even the simplest neural network architectures.

当网络通过对比其输入域中的抽象关系结构来学习类比时，会诱发最强大的类比推理能力。

本文研究了神经网络学习类比的能力，表明一个简单的神经网络能够解决某些类比问题

本文介绍了一种训练神经网络进行类比推理任务的方法，特别是考虑到视觉类比和符号类比。


Building chatbots that can accomplish goals such as booking a flight ticket is an unsolved problem in natural language understanding. Much progress has been made to build conversation models using techniques such as sequence2sequence modeling. One challenge in applying such techniques to building goal-oriented conversation models is that maximum likelihood-based models are not optimized toward accomplishing goals. Recently, many methods have been proposed to address this issue by optimizing a reward that contains task status or outcome. However, adding the reward optimization on the fly usually provides little guidance for language construction and the conversation model soon becomes decoupled from the language model. In this paper, we propose a new setting in goal-oriented dialogue system to tighten the gap between these two aspects by enforcing model level information isolation on individual models between two agents. Language construction now becomes an important part in reward optimization since it is the only way information can be exchanged. We experimented our models using self-play and results showed that our method not only beat the baseline sequence2sequence model in rewards but can also generate human-readable meaningful conversations of comparable quality.

一个以目标为导向的自娱自乐的神经对话模型 面向目标的对话生成的自娱自乐模型，旨在加强任务奖励和语言模型之间的耦合。 本文描述了一种利用自娱自乐来改进目标导向的对话系统的方法。


Search engine users nowadays heavily depend on query completion and correction to shape their queries.  Typically, the completion is done by database lookup which does not understand the context and cannot generalize to prefixes not in the database . In the paper, we propose to use unsupervised deep language models to complete and correct the queries given an arbitrary prefix .  We show how to address two main challenges that renders this method practical for large-scale deployment : 1) we propose a method for integrating error correction into the language model completion via a edit-distance potential and a variant of beam search that can exploit these potential functions; and 2) we show how to efficiently perform CPU-based computation to complete the queries, with error correction, in real time (generating top 10 completions within 16 ms). Experiments show that the method substantially increases hit rate over standard approaches, and is capable of handling tail queries.

使用字符级LSTM语言模型的实时搜索查询完成

本文介绍了包括前缀校正的查询完成方法，以及一些工程细节，以满足CPU上的特殊延迟要求。

作者提出了一种解决带有纠错功能的查询完成问题的算法，并采用基于字符级的RNN建模，对推理部分进行了优化，以实时实现目标。


RMSProp and ADAM continue to be extremely popular algorithms for training neural nets but their theoretical convergence properties have remained unclear. Further, recent work has seemed to suggest that these algorithms have worse generalization properties when compared to carefully tuned stochastic gradient descent or its momentum variants. In this work, we make progress towards a deeper understanding of ADAM and RMSProp in two ways. First, we provide proofs that these adaptive gradient algorithms are guaranteed to reach criticality for smooth non-convex objectives, and we give bounds on the running time.   Next we design experiments to empirically study the convergence and generalization properties of RMSProp and ADAM against Nesterov's Accelerated Gradient method on a variety of common autoencoder setups and on VGG-9 with CIFAR-10. Through these experiments we demonstrate the interesting sensitivity that ADAM has to its momentum parameter \beta_1. We show that at very high values of the momentum parameter (\beta_1 = 0.99) ADAM outperforms a carefully tuned NAG on most of our experiments, in terms of getting lower training and test losses. On the other hand, NAG can sometimes do better when ADAM's \beta_1 is set to the most commonly used value: \beta_1 = 0.9, indicating the importance of tuning the hyperparameters of ADAM to get better generalization performance.   We also report experiments on different autoencoders to demonstrate that NAG has better abilities in terms of reducing the gradient norms, and it also produces iterates which exhibit an increasing trend for the minimum eigenvalue of the Hessian of the loss function at the iterates.

在本文中，我们证明了（随机的和确定的）RMSProp和确定的ADAM对于平滑非凸目标的收敛临界性，并且我们证明了ADAM在自动编码器上的一个有趣的beta_1敏感性。

本文介绍了RMSProp和ADAM在光滑非凸函数情况下的收敛性分析


Recent advances in adversarial Deep Learning (DL) have opened up a new and largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems. We introduce a novel automated countermeasure called Parallel Checkpointing Learners (PCL) to thwart the potential adversarial attacks and significantly improve the reliability (safety) of a victim DL model. The proposed PCL methodology is unsupervised, meaning that no adversarial sample is leveraged to build/train parallel checkpointing learners. We formalize the goal of preventing adversarial attacks as an optimization problem to minimize the rarely observed regions in the latent feature space spanned by a DL network. To solve the aforementioned minimization problem, a set of complementary but disjoint checkpointing modules are trained and leveraged to validate the victim model execution in parallel. Each checkpointing learner explicitly characterizes the geometry of the input data and the corresponding high-level data abstractions within a particular DL layer. As such, the adversary is required to simultaneously deceive all the defender modules in order to succeed. We extensively evaluate the performance of the PCL methodology against the state-of-the-art attack scenarios, including Fast-Gradient-Sign (FGS), Jacobian Saliency Map Attack (JSMA), Deepfool, and Carlini&WagnerL2 algorithm. Extensive proof-of-concept evaluations for analyzing various data collections including MNIST, CIFAR10, and ImageNet corroborate the effectiveness of our proposed defense mechanism against adversarial samples.

设计针对对抗性攻击的无监督防御机制，对于确保防御的通用性至关重要。 本文介绍了一种在深度学习分类环境下检测对抗性例子的方法 本文提出了一种无监督的方法来检测神经网络的对抗性例子。


Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. 10 4 GPU hours) makes it difficult to directly search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize proxy tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present ProxylessNAS that can directly learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6× fewer parameters. On ImageNet, our model achieves 3.1% better top-1 accuracy than MobileNetV2, while being 1.2× faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for efficient CNN architecture design.

无代理的神经架构搜索，在大规模目标任务（ImageNet）上直接学习架构，同时将成本降低到与正常训练相同的水平。

本文讨论了架构搜索的问题，并特别寻求在不需要训练 "代理 "任务的情况下做到这一点，在这些任务中，问题通过更有限的优化、架构复杂性或数据集大小得到简化。


With the recently rapid development in deep learning, deep neural networks have been widely adopted in many real-life applications. However, deep neural networks are also known to have very little control over its uncertainty for test examples, which potentially causes very harmful and annoying consequences in practical scenarios. In this paper, we are particularly interested in designing a higher-order uncertainty metric for deep neural networks and investigate its performance on the out-of-distribution detection task proposed by~\cite{hendrycks2016baseline}. Our method first assumes there exists a underlying higher-order distribution $\mathcal{P}(z)$ , which generated label-wise distribution $\mathcal{P}(y)$ over classes on the K-dimension simplex, and then approximate such higher-order distribution via parameterized posterior function $p_{\theta}(z|x)$ under variational inference framework, finally we use the entropy of learned posterior distribution $p_{\theta}(z|x)$ as uncertainty measure to detect out-of-distribution examples. However , we identify the overwhelming over-concentration issue in such a framework, which greatly hinders the detection performance. Therefore , we further design a log-smoothing function to alleviate such issue to greatly increase the robustness of the proposed entropy-based uncertainty measure. Through comprehensive experiments on various datasets and architectures, our proposed variational Dirichlet framework with entropy-based uncertainty measure is consistently observed to yield significant improvements over many baseline systems.

基于变分推理的分布外检测新框架

描述了一种在DNN分类任务中量化不确定性的概率方法，该方法在分发外检测的任务中优于其他SOTA方法。

一个基于变分推理和先验狄利克雷分布的新的分布外检测框架，报告了几个数据集上的最新结果。

通过一种新的方法对分类概率的置信度分布进行近似，利用狄利克雷分布的变异推理，进行分布外检测。


Intelligent agents can learn to represent the action spaces of other agents simply by observing them act. Such representations help agents quickly learn to predict the effects of their own actions on the environment and to plan complex action sequences. In this work, we address the problem of learning an agent’s action space purely from visual observation. We use stochastic video prediction to learn a latent variable that captures the scene's dynamics while being minimally sensitive to the scene's static content. We introduce a loss term that encourages the network to capture the composability of visual sequences and show that it leads to representations that disentangle the structure of actions. We call the full model with composable action representations Composable Learned Action Space Predictor (CLASP). We show the applicability of our method to synthetic settings and its potential to capture action spaces in complex, realistic visual settings. When used in a semi-supervised setting, our learned representations perform comparably to existing fully supervised methods on tasks such as action-conditioned video prediction and planning in the learned action space, while requiring orders of magnitude fewer action labels. Project website: https://daniilidis-group.github.io/learned_action_spaces

我们从纯粹的视觉观察中学习一个代理行动空间的表示。我们使用了一种具有新的可组合性损失的循环潜变量方法。

提出了一个组合式的潜在变量模型，以学习预测在行动标签不丰富的情况下接下来会发生什么的模型。

一种基于变分IB的方法，直接从正在进行的动作视频中学习动作表征，实现了后续学习方法的更好效率，同时需要较少的动作标签视频。

本文提出了一种视频预测的方法，它可以自主地找到一个动作空间，对后续帧之间的差异进行编码。


When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation have been proposed, but typically only work for particular negotiation protocols. More general methods usually require human input or domain-specific data, and so do not scale. To address this, we propose a framework for training agents to negotiate and form teams using deep reinforcement learning. Importantly, our method makes no assumptions about the specific negotiation protocol, and is instead completely experience driven. We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments, demonstrating that our agents beat hand-crafted bots and reach negotiation outcomes consistent with fair solutions predicted by cooperative game theory. Additionally, we investigate how the physical location of agents influences negotiation outcomes.

强化学习可用于训练代理在许多谈判协议中进行团队组建谈判

本文研究了在所有代理必须合作完成任务的情况下的深度多代理RL（例如，搜索和救援，多人视频游戏），并使用简单的合作加权投票游戏来研究深度RL的功效，并将深度RL找到的解决方案与公平的解决方案进行比较。

一种在合作博弈论环境下谈判联盟的强化学习方法，可用于无限制训练模拟的情况。


Neural machine translation (NMT) models learn representations containing substantial linguistic information. However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons. We develop unsupervised methods for discovering important neurons in NMT models. Our methods rely on the intuition that different models learn similar properties, and do not require any costly external supervision. We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena. Finally, we show how to control NMT translations in predictable ways, by modifying activations of individual neurons.

寻找、分析和控制NMT中重要神经元的无监督方法

本文介绍了发现神经机器翻译系统中重要神经元的无监督方法，并分析了由这些神经元控制的语言学特性。

在机器翻译中对神经元进行排序的无监督方法，其中重要的神经元因此被识别并用于控制MT输出。


Recent state-of-the-art reinforcement learning algorithms are trained under the goal of excelling in one specific task. Hence, both environment and task specific knowledge are entangled into one framework. However, there are often scenarios where the environment (e.g. the physical world) is fixed while only the target task changes. Hence, borrowing the idea from hierarchical reinforcement learning, we propose a framework that disentangles task and environment specific knowledge by separating them into two units. The environment-specific unit handles how to move from one state to the target state; and the task-specific unit plans for the next target state given a specific task. The extensive results in simulators indicate that our method can efficiently separate and learn two independent units, and also adapt to a new task more efficiently than the state-of-the-art methods.

我们提出了一个DRL框架，该框架将任务和环境的具体知识分开。

作者建议将强化学习分解为一个PATH函数和一个GOAL函数

一个模块化的架构，目的是将特定环境的知识和特定任务的知识分离成不同的模块，与标准的A3C在广泛的任务中保持一致。


Modelling 3D scenes from 2D images is a long-standing problem in computer vision with implications in, e.g., simulation and robotics. We propose pix2scene, a deep generative-based approach that implicitly models the geometric properties of a scene from images. Our method learns the depth and orientation of scene points visible in images. Our model can then predict the structure of a scene from various, previously unseen view points. It relies on a bi-directional adversarial learning mechanism to generate scene representations from a latent code, inferring the 3D representation of the underlying scene geometry. We showcase a novel differentiable renderer to train the 3D model in an end-to-end fashion, using only images. We demonstrate the generative ability of our model qualitatively on both a custom dataset and on ShapeNet. Finally, we evaluate the effectiveness of the learned 3D scene representation in supporting a 3D spatial reasoning.

pix2scene：一种基于深度生成的方法，用于从图像中隐含地模拟三维场景的几何属性

探讨用神经识别模型中的surfels解释场景，并展示图像重建、合成和心理形状旋转的结果。

作者介绍了一种方法，在给定二维图像和摄像机姿势的情况下，使用self-superfised模型创建三维场景模型。


Identifying the relations that connect words is an important step towards understanding human languages and is useful for various NLP tasks such as knowledge base completion and analogical reasoning. Simple unsupervised operators such as vector offset between two-word embeddings have shown to recover some specific relationships between those words, if any. Despite this, how to accurately learn generic relation representations from word representations remains unclear. We model relation representation as a supervised learning problem and learn parametrised operators that map pre-trained word embeddings to relation representations. We propose a method for learning relation representations using a feed-forward neural network that performs relation prediction. Our evaluations on two benchmark datasets reveal that the penultimate layer of the trained neural network-based relational predictor acts as a good representation for the relations between words.

识别连接单词的关系对各种NLP任务都很重要。我们将关系表征建模为一个监督学习问题，并学习参数化操作符，将预先训练好的词嵌入映射到关系表征中。

本文提出了一种新的方法，只用预先训练好的词嵌入和在成对的词上操作的新的损失函数，就可以把词汇关系表示为向量。

当你已经有了预先训练好的词/实体嵌入，并且只对学习构成关系表征感兴趣时，对关系合成问题的一个新的解决方案。


Recurrent neural networks (RNNs) are important class of architectures among neural networks useful for language modeling and sequential prediction. However, optimizing RNNs is known to be harder compared to feed-forward neural networks. A number of techniques have been proposed in literature to address this problem. In this paper we propose a simple technique called fraternal dropout that takes advantage of dropout to achieve this goal. Specifically, we propose to train two identical copies of an RNN (that share parameters) with different dropout masks while minimizing the difference between their (pre-softmax) predictions. In this way our regularization encourages the representations of RNNs to be invariant to dropout mask, thus being robust. We show that our regularization term is upper bounded by the expectation-linear dropout objective which has been shown to address the gap due to the difference between the train and inference phases of dropout. We evaluate our model and achieve state-of-the-art results in sequence modeling tasks on two benchmark datasets - Penn Treebank and Wikitext-2. We also show that our approach leads to performance improvement by a significant margin in image captioning (Microsoft COCO) and semi-supervised (CIFAR-10) tasks.

我们建议用不同的丢弃掩码训练两个相同的循环神经网络副本（共享参数），同时最大限度地减少它们（pre-softmax）预测的差异。

介绍了Fraternal丢弃在收敛性方面对Expectation-linear丢弃的改进，并在一些任务和数据集上展示了Fraternal丢弃的效用。


We propose a novel approach for deformation-aware neural networks that learn the weighting and synthesis of dense volumetric deformation fields. Our method specifically targets the space-time representation of physical surfaces from liquid simulations. Liquids exhibit highly complex, non-linear behavior under changing simulation conditions such as different initial conditions. Our algorithm captures these complex phenomena in two stages: a first neural network computes a weighting function for a set of pre-computed deformations, while a second network directly generates a deformation field for refining the surface. Key for successful training runs in this setting is a suitable loss function that encodes the effect of the deformations, and a robust calculation of the corresponding gradients. To demonstrate the effectiveness of our approach, we showcase our method with several complex examples of flowing liquids with topology changes. Our representation makes it possible to rapidly generate the desired implicit surfaces. We have implemented a mobile application to demonstrate that real-time interactions with complex liquid effects are possible with our approach.

学习加权和变形的时空数据集，以实现液体行为的高效近似。

一个基于神经网络的模型被用来从密集注册的4D隐性表面对新的场景条件进行插值模拟，用于结构化场景。

本文介绍了一种耦合的深度学习方法，用于生成对实时决策支持应用有用的现实液体模拟数据。

本文介绍了一种用于物理模拟的深度学习方法，它结合了两个网络来合成代表三维物理模拟的4D数据


This is an empirical paper which constructs color invariant networks and evaluates their performances on a realistic data set. The paper studies the simplest possible case of color invariance: invariance under pixel-wise permutation of the color channels. Thus the network is aware not of the specific color object, but its colorfulness. The data set introduced in the paper consists of images showing crashed cars from which ten classes were extracted. An additional annotation was done which labeled whether the car shown was red or non-red.   The networks were evaluated by their performance on the classification task. With the color annotation we altered the color ratios  in the training data and analyzed the generalization capabilities of the networks on the unaltered test data. We further split the test data in red and non-red cars and did a similar evaluation. It is shown in the paper that an pixel-wise ordering of the rgb-values of the images performs better or at least similarly for small deviations from the true color ratios. The limits of these networks are also discussed.

我们在一个新的现实数据集上构建并评估颜色不变的神经网络

提出了一种使用于图像识别的神经网络具有颜色不变性的方法，并在cifar 10数据集上对其进行了评估。

作者研究了一种修改过的输入层，它导致了颜色不变的网络，并表明某些颜色不变的输入层可以提高来自与训练图像不同颜色分布的测试图像的准确性。

作者在图像上测试了一个CNN，该CNN的颜色通道被修改为不受排列组合的影响，性能没有下降太多。


Expressive efficiency refers to the relation between two architectures A and B, whereby any function realized by B could be replicated by A, but there exists functions realized by A, which cannot be replicated by B unless its size grows significantly larger. For example, it is known that deep networks are exponentially efficient with respect to shallow networks, in the sense that a shallow network must grow exponentially large in order to approximate the functions represented by a deep network of polynomial size. In this work, we extend the study of expressive efficiency to the attribute of network connectivity and in particular to the effect of "overlaps" in the convolutional process, i.e., when the stride of the convolution is smaller than its filter size (receptive field).  To theoretically analyze this aspect of network's design, we focus on a well-established surrogate for ConvNets called Convolutional Arithmetic Circuits (ConvACs), and then demonstrate empirically that our results hold for standard ConvNets as well. Specifically, our analysis shows that having overlapping local receptive fields, and more broadly denser connectivity, results in an exponential increase in the expressive capacity of neural networks. Moreover, while denser connectivity can increase the expressive capacity, we show that the most common types of modern architectures already exhibit exponential increase in expressivity, without relying on fully-connected layers.

我们分析了卷积网络的感受野之间的重叠程度如何影响其表达能力。

本文通过考虑带有乘积池的线性激活，研究了DNN卷积层中的 "重叠 "所提供的表达能力。

本文分析了卷积算术电路的表现力，并表明需要指数级的大量非重叠的ConvAC来近似重叠的ConvAC的网格张量。


We provide a theoretical algorithm for checking local optimality and escaping saddles at nondifferentiable points of empirical risks of two-layer ReLU networks. Our algorithm receives any parameter value and returns: local minimum, second-order stationary point, or a strict descent direction. The presence of M data points on the nondifferentiability of the ReLU divides the parameter space into at most 2^M regions, which makes analysis difficult. By exploiting polyhedral geometry, we reduce the total computation down to one convex quadratic program (QP) for each hidden node, O(M) (in)equality tests, and one (or a few) nonconvex QP. For the last QP, we show that our specific problem can be solved efficiently, in spite of nonconvexity. In the benign case, we solve one equality constrained QP, and we prove that projected gradient descent solves it exponentially fast. In the bad case, we have to solve a few more inequality constrained QPs, but we prove that the time complexity is exponential only in the number of inequality constraints. Our experiments show that either benign case or bad case with very few inequality constraints occurs, implying that our algorithm is efficient in most cases.

检验局部最优性和提取单隐层ReLU网络的经验风险的不可微分点的下降方向的理论算法。

提出了一种检查给定点是否是广义二阶静止点的算法。

一种理论上的算法，涉及解决凸和非凸的二次方程序，用于在训练两层ReLU网络时检查局部最优性和摆脱鞍座。

作者提出了一种方法来检查一个点是否是静止点，然后将静止点分类为局部最小点或二阶静止点。


We present a new technique for learning visual-semantic embeddings for cross-modal retrieval.   Inspired by the use of hard negatives in structured prediction, and ranking loss functions used in retrieval, we introduce a simple change to common loss functions used to learn multi-modal embeddings.   That, combined with fine-tuning and the use of augmented data, yields significant gains in retrieval performance.   We showcase our approach, dubbed VSE++, on the MS-COCO and Flickr30K datasets, using ablation studies and comparisons with existing methods.   On MS-COCO our approach outperforms state-of-the-art methods by 8.8% in caption retrieval, and 11.3% in image retrieval (based on R@1).

一种基于相对较硬的底片的新损失，在图像标题检索中取得了最先进的性能。

使用适用于最难否定的三联体而不是所有三联体的平均数的三联体损失，学习句子和图像的联合嵌入


We present DANTE, a novel method for training neural networks, in particular autoencoders, using the alternating minimization principle. DANTE provides a distinct perspective in lieu of traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convex optimization techniques to cast autoencoder training as a bi-quasi-convex optimization problem. We show that for autoencoder configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations very effectively. DANTE effortlessly extends to networks with multiple hidden layers and varying network configurations. In experiments on standard datasets, autoencoders trained using the proposed method were found to be very promising when compared to those trained using traditional backpropagation techniques, both in terms of training speed, as well as feature extraction and reconstruction performance.

我们利用交替最小化原理，提供了一种有效的新技术来训练深度自动编码器。

训练自动编码器和编码器-解码器网络的交替最小化框架

作者探索了一种训练自动编码器的交替优化方法，将每一层视为广义线性模型，并建议在每个阶段使用随机归一化GD作为最小化算法。


We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the same underlying causal mechanisms. We compare our algorithms with those in the extant literature using extensive simulation studies based on large-scale voter persuasion experiments and the MNIST database. Our methods can perform an order of magnitude better than existing benchmarks while using a fraction of the data.

使用神经网络估计因果效应的转移学习。

开发了在不同环境下通过辅助数据集估计条件平均治疗效果的算法，包括有和没有基础学习器。

作者提出了解决估计CATE函数的转移学习的新任务的方法，并使用一个合成环境和一个真实世界的实验数据集对其进行了评估。

使用神经网络回归和比较转移学习框架来估计字符串无视性假设下的条件平均处理效应


Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing. We here propose LeMoNADe, a new exploratory data analysis method that facilitates hunting for motifs in calcium imaging videos, the dominant microscopic functional imaging modality in neurophysiology. Our nonparametric method extracts motifs directly from videos, bypassing the difficult intermediate step of spike extraction. Our technique augments variational autoencoders with a discrete stochastic node, and we show in detail how a differentiable reparametrization and relaxation can be used. An evaluation on simulated data, with available ground truth, reveals excellent quantitative performance. In real video data acquired from brain slices, with no ground truth available, LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses for more focused biological investigations.

我们提出了LeMoNADe，一种直接在钙成像视频上操作的端到端学习基序检测方法。

本文提出了一个VAE式的模型，用于识别钙成像视频中的图案，依赖于伯努利变量，并需要Gumbel-softmax技巧进行推理。


A noisy and diverse demonstration set may hinder the performances of an agent aiming to acquire certain skills via imitation learning. However, state-of-the-art imitation learning algorithms often assume the optimality of the given demonstration set.  In this paper, we address such optimal assumption by learning only from the most suitable demonstrations in a given set. Suitability of a demonstration is estimated by whether imitating it produce desirable outcomes for achieving the goals of the tasks. For more efficient demonstration suitability assessments, the learning agent should be capable of imitating a demonstration as quick as possible, which shares similar spirit with fast adaptation in the meta-learning regime. Our framework, thus built on top of Model-Agnostic Meta-Learning, evaluates how desirable the imitated outcomes are, after adaptation to each demonstration in the set. The resulting assessments hence enable us to select suitable demonstration subsets for acquiring better imitated skills. The videos related to our experiments are available at: https://sites.google.com/view/deepdj

我们提出了一个框架，通过元训练示范适用性评估器，从嘈杂的示范集中模仿学习一个好策略。

贡献了一个基于MAML的模仿学习算法，该算法可自动确定所提供的示范是否 "合适"。

一种从包括无用行为的演示集合中进行模仿学习的方法，该方法通过其在元训练时提供的性能收益来选择有用的演示。


We introduce causal implicit generative models (CiGMs): models that allow sampling from not only the true observational but also the true interventional distributions. We show that adversarial training can be used to learn a CiGM, if the generator architecture is structured based on a given causal graph. We consider the application of conditional and interventional sampling of face images with binary feature labels, such as mustache, young. We preserve the dependency structure between the labels with a given causal graph. We devise a two-stage procedure for learning a CiGM over the labels and the image. First we train a CiGM over the binary labels using a  Wasserstein GAN where the generator neural network is consistent with the causal graph between the labels. Later, we combine this with a conditional GAN to generate images conditioned on the binary labels. We propose two new conditional GAN architectures: CausalGAN and CausalBEGAN. We show that the optimal generator of the CausalGAN, given the labels, samples from the image distributions conditioned on these labels. The conditional GAN combined with a trained CiGM for the labels is then a CiGM over the labels and the generated image. We show that the proposed architectures can be used to sample from observational and interventional image distributions, even for interventions which do not naturally occur in the dataset.

我们引入了因果隐含生成模型，它可以从条件分布和干预分布中取样，还提出了两个新的条件GAN，我们用它来训练它们。

一种结合随意图的方法，描述了标签的依赖结构和两个条件性GAN架构，该架构生成了以二进制标签为条件的图像。

作者解决了在给定图像标签之间的因果结构时，从观察数据中学习图像变量和图像本身之间的因果模型的问题。


Self-normalizing discriminative models approximate the normalized probability of a class without having to compute the partition function. This property is useful to computationally-intensive neural network classifiers, as the cost of computing the partition function grows linearly with the number of classes and may become prohibitive. In particular, since neural language models may deal with up to millions of classes, their self-normalization properties received notable attention. Several  recent studies empirically found that language models, trained using Noise Contrastive Estimation (NCE), exhibit self-normalization, but could not explain why. In this study, we provide a theoretical justification to this property by viewing  NCE as a low-rank matrix approximation. Our empirical investigation compares NCE to the alternative explicit approach for self-normalizing language models. It also uncovers a surprising negative correlation between self-normalization and  perplexity, as well as some regularity in the observed errors that may potentially be used for improving self-normalization algorithms in the future.

我们证明NCE是自我归一化的，并在数据集上证明了这一点。

提出了NCE的自我归一化证明，因为它是归一化条件概率矩阵的低秩矩阵近似，是低秩近似的结果。

本文考虑了自我归一化模型的问题，并通过用矩阵因式分解解释NCE来解释自我归一化机制。


Learning word representations from large available corpora relies on the distributional hypothesis that words present in similar contexts tend to have similar meanings. Recent work has shown that word representations learnt in this manner lack sentiment information which, fortunately, can be leveraged using external knowledge. Our work addresses the question: can affect lexica improve the word representations learnt from a corpus? In this work, we propose techniques to incorporate affect lexica, which capture fine-grained information about a word's psycholinguistic and emotional orientation, into the training process of Word2Vec SkipGram, Word2Vec CBOW and GloVe methods using a joint learning approach. We use affect scores from Warriner's affect lexicon to regularize the vector representations learnt from an unlabelled corpus. Our proposed method outperforms previously proposed methods on standard tasks for word similarity detection, outlier detection and sentiment detection. We also demonstrate the usefulness of our approach for a new task related to the prediction of formality, frustration and politeness in corporate communication.

用情感信息来丰富词的嵌入，可以提高它们在情感预测任务上的表现。

提议使用情感词库来改进词嵌入，使其优于标准的Word2vec和Glove。

本文提出将量化词语影响的语义资源的信息整合到基于文本的词语嵌入算法中，以使语言模型更能反映语义和语用现象。

本文介绍了对word2vec和GloVe损失函数的修改，以纳入情感词库，促进情感敏感词嵌入的学习。


Different kinds of representation learning techniques on graph have shown significant effect in downstream machine learning tasks. Recently, in order to inductively learn representations for graph structures that is unobservable during training, a general framework with sampling and aggregating (GraphSAGE) was proposed by Hamilton and Ying and had been proved more efficient than transductive methods on fileds like transfer learning or evolving dataset. However, GraphSAGE is uncapable of selective neighbor sampling and lack of memory of known nodes that've been trained. To address these problems, we present an unsupervised method that samples neighborhood information attended by co-occurring structures and optimizes a trainable global bias as a representation expectation for each node in the given graph. Experiments show that our approach outperforms the state-of-the-art inductive and unsupervised methods for representation learning on graphs.

对于无监督和归纳式网络嵌入，我们提出了一种新的方法，通过利用双注意力架构和引入全局偏见，分别探索最相关的邻居和保留先前学习的节点知识。

这就提出了对GraphSAGE的扩展，在局部聚合函数中使用全局嵌入偏置矩阵，并采用一种方法对感兴趣的节点进行采样。


Learning distributed representations for nodes in graphs is a crucial primitive in network analysis with a wide spectrum of applications. Linear graph embedding methods learn such representations by optimizing the likelihood of both positive and negative edges while constraining the dimension of the embedding vectors. We argue that the generalization performance of these methods is not due to the dimensionality constraint as commonly believed, but rather the small norm of embedding vectors. Both theoretical and empirical evidence are provided to support this argument: (a) we prove that the generalization error of these methods can be bounded by limiting the norm of vectors, regardless of the embedding dimension; (b) we show that the generalization performance of linear graph embedding methods is correlated with the norm of embedding vectors, which is small due to the early stopping of SGD and the vanishing gradients. We performed extensive experiments to validate our analysis and showcased the importance of proper norm regularization in practice.

我们认为，线性图嵌入的泛化并不是由于维度的约束，而是由于嵌入向量的小范数。

作者表明，线性图嵌入方法的泛化误差受嵌入向量的范数约束，而不是维度约束。

作者对学习图嵌入的泛化性能提出了一个理论约束，并认为坐标的范数决定了所学表征的成功。


Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other algorithms, and we characterize the set of two-state optimization algorithms that QHM can recover. Finally, we propose a QH variant of Adam called QHAdam, and we empirically demonstrate that our algorithms lead to significantly improved training in a variety of settings, including a new state-of-the-art result on WMT16 EN-DE. We hope that these empirical results, combined with the conceptual and practical simplicity of QHM and QHAdam, will spur interest from both practitioners and researchers. Code is immediately available.

混合普通SGD和动量（或用Adam做类似的事情），获得巨大利润。

本文提出了对SGD和Adam的简单修改，称为QH变体，可以恢复父方法和其他一系列的优化技巧。

经典动量的一个变体，它采取动量和梯度更新的加权平均，以及对其与其他基于动量的优化方案之间关系的评估。


Reinforcement Learning (RL) can model complex behavior policies for goal-directed sequential decision making tasks. A hallmark of RL algorithms is Temporal Difference (TD) learning: value function for the current state is moved towards a bootstrapped target that is estimated using the next state's value function. lambda-returns define the target of the RL agent as a weighted combination of rewards estimated by using multiple many-step look-aheads. Although mathematically tractable, the use of  exponentially decaying weighting of n-step returns based targets in lambda-returns is a rather ad-hoc design choice. Our major contribution  is that we propose a generalization of lambda-returns called Confidence-based Autodidactic Returns (CAR), wherein the RL agent learns the weighting of the n-step returns in an end-to-end manner. In contrast to lambda-returns wherein the RL agent is restricted to use an exponentially decaying weighting scheme, CAR allows the agent to learn to decide how much it wants to weigh the n-step returns based targets. Our experiments, in addition to showing the efficacy of CAR, also empirically demonstrate that using sophisticated weighted mixtures of multi-step returns (like CAR and lambda-returns) considerably outperforms the use of n-step returns. We perform our experiments on the  Asynchronous Advantage Actor Critic (A3C) algorithm in the Atari 2600 domain.

通过允许RL代理决定它要权衡n个步骤的每一个回报的程度来概括lambda-returns的一种新方法。

扩展了带有lambda回报的A3C算法，并提出一种学习回报权重的方法。

作者提出了基于置信度的自律回报，这是一种深度学习RL方法，用于调整TD(lambda)-like价值估计中的资格向量的权重，以有利于对状态的更稳定的估计。


Current end-to-end deep learning driving models have two problems: (1) Poor  generalization ability of unobserved driving environment when diversity of train-  ing driving dataset is limited (2) Lack of accident explanation ability when driving  models don’t work as expected. To tackle these two problems, rooted on the be-  lieve that knowledge of associated easy task is benificial for addressing difficult  task, we proposed a new driving model which is composed of perception module  for see and think and driving module for behave, and trained it with multi-task  perception-related basic knowledge and driving knowledge stepwisely.  Specifi-  cally segmentation map and depth map (pixel level understanding of images) were  considered as what & where and how far knowledge for tackling easier driving-  related perception problems before generating final control commands for difficult  driving task. The results of experiments demonstrated the effectiveness of multi-  task perception knowledge for better generalization and accident explanation abil-  ity. With our method the average sucess rate of finishing most difficult navigation  tasks in untrained city of CoRL test surpassed current benchmark method for 15  percent in trained weather and 20 percent in untrained weathers.

我们提出了一个新的自驾游模型，该模型由观察和思考的感知模块和行为的驾驶模块组成，以获得更好的泛化和事故解释能力。

提出了一个多任务学习架构，用于深度和分割图的估计以及使用感知模块和驾驶决策模块的驾驶预测。

一种修改后的端到端结构的方法，具有更好的概括和解释能力，对不同的测试环境更加稳健，并且有解码器输出，可以帮助调试模型。

作者提出了一个用于端到端驾驶的多任务卷积神经网络，并提供了用CARLA开源模拟器进行的评估，显示在新的驾驶条件下比基线有更好的泛化性能。


Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework – a generic methodology allowing contemporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarsest graph and refines the embeddings to the original graph through a novel graph convolution neural network that it learns. The proposed MILE framework is agnostic to the underlying graph embedding techniques and can be applied to many existing graph embedding methods without modifying them. We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs. Experimental results on five large-scale datasets demonstrate that MILE significantly boosts the speed (order of magnitude) of graph embedding while also often generating embeddings of better quality for the task of node classification. MILE can comfortably scale to a graph with 9 million nodes and 40 million edges, on which existing methods run out of memory or take too long to compute on a modern workstation.

一个通用框架，将现有的图嵌入技术扩展到大型图。

本文提出了一个多级嵌入框架，应用于现有的网络嵌入方法之上，以便以更快的速度扩展到大规模的网络。

作者提出了一个提高嵌入质量的大规模图形嵌入的三阶段框架。


Anomaly detection discovers regular patterns in unlabeled data and identifies the non-conforming data points, which in some cases are the result of malicious attacks by adversaries. Learners such as One-Class Support Vector Machines (OCSVMs) have been successfully in anomaly detection, yet their performance may degrade significantly in the presence of sophisticated adversaries, who target the algorithm itself by compromising the integrity of the training data. With the rise in the use of machine learning in mission critical day-to-day activities where errors may have significant consequences, it is imperative that machine learning systems are made secure. To address this, we propose a defense mechanism that is based on a contraction of the data, and we test its effectiveness using OCSVMs. The proposed approach introduces a layer of uncertainty on top of the OCSVM learner, making it infeasible for the adversary to guess the specific configuration of the learner. We theoretically analyze the effects of adversarial perturbations on the separating margin of OCSVMs and provide empirical evidence on several benchmark datasets, which show that by carefully contracting the data in low dimensional spaces, we can successfully identify adversarial samples that would not have been identifiable in the original dimensional space. The numerical results show that the proposed method improves OCSVMs performance significantly (2-7%)

通过对数据进行选择性的非线性转换，以较低的维度增加OCSVMs对目标性、完整性攻击的抵抗力的新方法。

作者提出了对基于单类SVM的异常检测器的安全攻击的防御措施

本文探讨了如何利用随机投影来使OCSVM对对抗性扰动的训练数据具有稳健性。


In this paper, we present a layer-wise learning of stochastic neural networks (SNNs) in an information-theoretic perspective. In each layer of an SNN, the compression and the relevance are defined to quantify the amount of information that the layer contains about the input space and the target space, respectively. We jointly optimize the compression and the relevance of all parameters in an SNN to better exploit the neural network's representation. Previously, the Information Bottleneck (IB) framework (\cite{Tishby99}) extracts relevant information for a target variable. Here, we propose Parametric Information Bottleneck (PIB) for a neural network by utilizing (only) its model parameters explicitly to approximate the compression and the relevance. We show that, as compared to the maximum likelihood estimate (MLE) principle, PIBs : (i) improve the generalization of neural networks in classification tasks, (ii) push the representation of neural networks closer to the optimal information-theoretical representation in a faster manner.

利用信息瓶颈原理学习更好的神经网络表征

提出了一种基于信息瓶颈框架的学习方法，深层网络的隐藏层在压缩输入X的同时保持足够的信息来预测输出Y。

本文提出了一种遵循类似于信息瓶颈的信息相关性/压缩框架来训练随机神经网络的新方法。


The maximum mean discrepancy (MMD) between two probability measures P  and Q is a metric that is zero if and only if all moments of the two measures  are equal, making it an appealing statistic for two-sample tests. Given i.i.d. samples  from P and Q, Gretton et al. (2012) show that we can construct an unbiased  estimator for the square of the MMD between the two distributions. If P is a  distribution of interest and Q is the distribution implied by a generative neural  network with stochastic inputs, we can use this estimator to train our neural network.  However, in practice we do not always have i.i.d. samples from our target  of interest. Data sets often exhibit biases—for example, under-representation of  certain demographics—and if we ignore this fact our machine learning algorithms  will propagate these biases. Alternatively, it may be useful to assume our data has  been gathered via a biased sample selection mechanism in order to manipulate  properties of the estimating distribution Q.  In this paper, we construct an estimator for the MMD between P and Q when we  only have access to P via some biased sample selection mechanism, and suggest  methods for estimating this sample selection mechanism when it is not already  known. We show that this estimator can be used to train generative neural networks  on a biased data sample, to give a simulator that reverses the effect of that  bias.

我们提出了一个最大平均差异的估计器，适用于目标分布只能通过有偏见的样本选择程序获得的情况，并表明它可以用于生成网络以纠正这种偏见。

提出了一个重要性加权的MMD估计器，以估计基于根据已知或估计的未知方案偏向的样本的分布之间的MMD。

作者解决了MMD-GANs中的样本选择偏差问题，并提出使用加权最大平均差异来估计两个分布之间的MMD。

本文介绍了对用于训练生成网络的目标的修改，该目标具有MMD对抗性


We propose Bayesian Deep Q-Network  (BDQN), a  practical Thompson sampling based Reinforcement Learning (RL) Algorithm. Thompson sampling allows for targeted exploration in high dimensions through posterior sampling but is usually computationally expensive. We address this limitation by introducing uncertainty only at the output layer of the network through a Bayesian Linear Regression (BLR) model, which can be trained with fast closed-form updates and its samples can be drawn efficiently through the Gaussian distribution. We apply our method to a wide range of Atari Arcade Learning Environments. Since BDQN carries out more efficient exploration, it is able to reach higher rewards substantially faster than a key baseline, DDQN.

使用贝叶斯回归来估计Q函数的后验，并将汤普森抽样作为一种有针对性的探索策略，有效地权衡探索和开发。

作者提出了一种新的深层RL的探索算法，他们应用贝叶斯线性回归与DQN网络最后一层的特征来估计每个动作的Q函数。

作者描述了如何使用带有汤普森采样的贝叶斯神经网络在q-learning中进行高效探索，并提出了一种优于ε-greedy探索方法的方法。


In this work, we propose the polynomial convolutional neural network (PolyCNN), as a new design of a weight-learning efficient variant of the traditional CNN. The biggest advantage of the PolyCNN is that at each convolutional layer, only one convolutional filter is needed for learning the weights, which we call the seed filter, and all the other convolutional filters are the polynomial transformations of the seed filter, which is termed as an early fan-out. Alternatively, we can also perform late fan-out on the seed filter response to create the number of response maps needed to be input into the next layer. Both early and late fan-out allow the PolyCNN to learn only one convolutional filter at each layer, which can dramatically reduce the model complexity by saving 10x to 50x parameters during learning. While being efficient during both training and testing, the PolyCNN does not suffer performance due to the non-linear polynomial expansion which translates to richer representational power within the convolutional layers. By allowing direct control over model complexity, PolyCNN provides a flexible trade-off between performance and efficiency. We have verified the on-par performance between the proposed PolyCNN and the standard CNN on several visual datasets, such as MNIST, CIFAR-10, SVHN, and ImageNet.

PolyCNN只需要在每层学习一个种子卷积滤波器。这是传统CNN的一个有效变体，具有同等的性能。

试图通过使用滤波器的多项式变换来减少CNN模型参数的数量，以创造放大滤波器的响应。

作者提出了一种权重共享架构，用于减少带有种子滤波器的卷积神经网络参数数量


Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is non-trivial in practice. Although kernel selection for the two-sample test has been studied, the insufficient samples in change point detection problem hinder the success of those developed kernel selection algorithms. In this paper, we propose KL-CPD, a novel kernel learning framework for time series CPD that optimizes a lower bound of test power via an auxiliary generative model. With deep kernel parameterization, KL-CPD endows kernel two-sample test with the data-driven kernel to detect different types of change-points in real-world applications. The proposed approach significantly outperformed other state-of-the-art methods in our comparative evaluation of benchmark datasets and simulation studies.

在本文中，我们提出了KL-CPD，一个用于时间序列CPD的新型核学习框架，通过辅助生成模型作为异常分布的替代物，优化测试功率的下限。

描述了一种新的方法来优化内核的选择，以提高测试能力，并显示出比其他方法更好。


Theories in cognitive psychology postulate that humans use similarity as a basis  for object categorization. However, work in image classification generally as-  sumes disjoint and equally dissimilar classes to achieve super-human levels of  performance on certain datasets. In our work, we adapt notions of similarity using  weak labels over multiple hierarchical levels to boost classification performance.  Instead of pitting clustering directly against classification, we use a warm-start  based evaluation to explicitly provide value to a clustering representation by its  ability to aid classification. We evaluate on CIFAR10 and a fine-grained classifi-  cation dataset to show improvements in performance with the procedural addition  of intermediate losses and weak labels based on multiple hierarchy levels. Further-  more, we show that pretraining AlexNet on hierarchical weak labels in conjunc-  tion with intermediate losses outperforms a classification baseline by over 17% on  a subset of Birdsnap dataset. Finally, we show improvement over AlexNet trained  using ImageNet pre-trained weights as initializations which further supports our   claim of the importance of similarity.

先聚类再分类；使用弱标签来改进分类

建议在深度网络的多个层次上使用基于聚类的损失函数，以及使用标签空间的分层结构来训练更好的表征。

本文利用分层标签信息对神经网络训练中的中间表征施加额外损失。


Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully observed state and must compensate for partial or non-Markovian observations by using finite-length frame-history observations or recurrent networks. In this work, we propose a new deep reinforcement learning algorithm based on counterfactual regret minimization that iteratively updates an approximation to a cumulative clipped advantage function and is robust to partially observed state. We demonstrate that on several partially observed reinforcement learning tasks, this new class of algorithms can substantially outperform strong baseline methods: on Pong with single-frame observations, and on the challenging Doom (ViZDoom) and Minecraft (Malmö) first-person navigation benchmarks.

基于优势的遗憾最小化是一种新的深度强化学习算法，对部分可观察的任务特别有效，如《毁灭战士》和《Minecraft》中的第一人称导航。

本文介绍了Deep RL领域的反事实遗憾最小化的概念和一种叫做ARM的算法，该算法可以更好地处理部分可观察性。

本文基于反事实遗憾最小化的思想，提供了一个受博弈论启发的策略梯度算法的变体，并声称该方法可以比标准方法更好地处理部分可观察域。


Recent deep multi-task learning (MTL) has been witnessed its success in alleviating data scarcity of some task by utilizing domain-specific knowledge from related tasks. Nonetheless, several major issues of deep MTL, including the effectiveness of sharing mechanisms, the efficiency of model complexity and the flexibility of network architectures, still remain largely unaddressed. To this end, we propose a novel generalized latent-subspace based knowledge sharing mechanism for linking task-specific models, namely tensor ring multi-task learning (TRMTL). TRMTL has a highly compact representation, and it is very effective in transferring task-invariant knowledge while being super flexible in learning task-specific features, successfully mitigating the dilemma of both negative-transfer in lower layers and under-transfer in higher layers. Under our TRMTL, it is feasible for each task to have heterogenous input data dimensionality or distinct feature sizes at different hidden layers. Experiments on a variety of datasets demonstrate our model is capable of significantly improving each single task’s performance, particularly favourable in scenarios where some of the tasks have insufficient data.

一个适应张量环状表征的深度多任务学习模型

用于多任务学习的张量环公式的变体，通过共享一些TT核心来学习 "共同任务"，同时为每个独立任务学习单独的TT核心。


Neural Processes (NPs) (Garnelo et al., 2018) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled.

通过将注意力纳入神经过程，建立了一个学习随机过程的条件分布的回归模型。

提出通过在确定性路径中加入注意力机制来解决神经过程法中的欠拟合问题。

对神经过程框架的扩展，增加了一个基于注意力的调节机制，使该模型能够更好地捕捉调节集的依赖性。

作者通过纳入自我注意来丰富上下文点的特征，以及交叉注意来产生特定查询的表征来扩展神经过程。他们解决了NP的欠拟合问题，并表明ANP比NP收敛得更好、更快。


Deconvolutional layers have been widely used in a variety of deep  models for up-sampling, including encoder-decoder networks for  semantic segmentation and deep generative models for unsupervised  learning. One of the key limitations of deconvolutional operations  is that they result in the so-called checkerboard problem. This is  caused by the fact that no direct relationship exists among adjacent  pixels on the output feature map. To address this problem, we  propose the pixel deconvolutional layer (PixelDCL) to establish  direct relationships among adjacent pixels on the up-sampled feature  map. Our method is based on a fresh interpretation of the regular  deconvolution operation. The resulting PixelDCL can be used to  replace any deconvolutional layer in a plug-and-play manner without  compromising the fully trainable capabilities of original models.  The proposed PixelDCL may result in slight decrease in efficiency,  but this can be overcome by an implementation trick. Experimental  results on semantic segmentation demonstrate that PixelDCL can  consider spatial features such as edges and shapes and yields more  accurate segmentation outputs than deconvolutional layers. When used  in image generation tasks, our PixelDCL can largely overcome the  checkerboard problem suffered by regular deconvolution operations.

通过建立像素间的依赖关系解决去反卷积层中的棋盘问题

这项工作为卷积神经网络提出了像素去卷积层，作为缓解棋盘效应的一种方式。

一种新的技术来概括标准CNN架构中使用的解卷积操作，它提出对相邻的像素特征进行顺序预测，从而为解卷积层带来更多的空间平滑输出。


In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem. To this end, a quantizing prior that leads to a multi-modal, sparse posterior distribution over weights, is introduced and a differentiable Kullback-Leibler divergence approximation for this prior is derived. After training with Variational Network Quantization, weights can be replaced by deterministic quantization values with small to negligible loss of task accuracy (including pruning by setting weights to 0). The method does not require fine-tuning after quantization. Results are shown for ternary quantization on LeNet-5 (MNIST) and DenseNet (CIFAR-10).

我们使用具有多模态、稀疏性诱导先验的变分贝叶斯推理对神经网络权重进行量化和修整。

提出用连续穗状突变1/abs的混合物作为贝叶斯神经网络的先验，并证明了minist和cifar-10的相对稀疏化的凸网络的良好性能。

本文提出了一种变分贝叶斯方法，以一种原则性的方式将神经网络权重量化为三元值后进行训练。


Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices. Extensive research work have been conducted on DNN model compression or pruning. However, most of the previous work took heuristic approaches. This work proposes a progressive weight pruning approach based on ADMM (Alternating Direction Method of Multipliers), a powerful technique to deal with non-convex optimization problems with potentially combinatorial constraints. Motivated by dynamic programming, the proposed method reaches extremely high pruning rate by using partial prunings with moderate pruning rates. Therefore, it resolves the accuracy degradation and long convergence time problems when pursuing extremely high pruning ratios. It achieves up to 34× pruning rate for ImageNet dataset and 167× pruning rate for MNIST dataset, significantly higher than those reached by the literature work. Under the same number of epochs, the proposed method also achieves faster convergence and higher compression rates. The codes and pruned DNN models are released in the anonymous link bit.ly/2zxdlss.

我们实施了一种DNN权重修剪方法，实现了最高的修剪率。

本文专注于神经网络压缩的权重修剪，实现了AlexNet的30倍压缩率和ImageNet的VGG。

一种渐进式修剪技术，对权重参数施加结构稀疏性约束，并将优化改写为ADMM框架，实现了比投影梯度下降更高的精度。


In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network. This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models.

本文提出了一个新的深度学习架构，用于解决稀疏和不规则采样的多变量时间序列的监督学习问题。

提出了一个对稀疏的、不规则采样的时间序列数据进行预测的框架，使用一个插值模块，在使用平滑插值、非平滑插值和强度中对缺失值进行建模。

利用半参数插值网络和预测网络，解决了稀疏和不规则采样的多变量时间序列的监督学习问题。