Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time.To overcome this problem, we present a simple and effective method self-ensemble label filtering (SELF) to progressively filter out the wrong labels during training.Our method improves the task performance by gradually allowing supervision only from the potentially non-noisy (clean) labels and stops learning on the filtered noisy labels.For the filtering, we form running averages of predictions over the entire training dataset using the network output at different training epochs.We show that these ensemble estimates yield more accurate identification of inconsistent predictions throughout training than the single estimates of the network at the most recent training epoch.While filtered samples are removed entirely from the supervised training loss, we dynamically leverage them via semi-supervised learning in the unsupervised loss.We demonstrate the positive effect of such an approach on various image classification tasks under both symmetric and asymmetric label noise and at different noise ratios.It substantially outperforms all previous works on noise-aware learning across different datasets and can be applied to a broad set of network architectures.

我们提出了一个自我组装框架，以在有噪音的标记数据集下训练更强大的深度学习模型。

本文提出了 "自组合标签过滤"，用于有噪音标签的学习，其中标签噪音是与实例无关的，这将产生更准确的不一致预测的识别。

本文提出了一种从有噪声标签的数据中学习的算法，该算法在更新模型和删除看起来有噪音标签的样本之间交替进行。


Long training times of deep neural networks are a bottleneck in machine learning research.The major impediment to fast training is the quadratic growth of both memory and compute requirements of dense and convolutional layers with respect to their information bandwidth.Recently, training `a priori' sparse networks has been proposed as a method for allowing layers to retain high information bandwidth, while keeping memory and compute low.However, the choice of which sparse topology should be used in these networks is unclear.In this work, we provide a theoretical foundation for the choice of intra-layer topology.First, we derive a new sparse neural network initialization scheme that allows us to explore the space of very deep sparse networks.Next, we evaluate several topologies and show that seemingly similar topologies can often have a large difference in attainable accuracy.To explain these differences, we develop a data-free heuristic that can evaluate a topology independently from the dataset the network will be trained on.We then derive a set of requirements that make a good topology, and arrive at a single topology that satisfies all of them.

我们研究了在训练前修剪DNN的问题，并对训练先验稀疏网络时应使用哪种拓扑结构提供了答案。

作者提出用疏散连接的线性层取代密集层，并提出了一种通过测量疏散层对密集层的随机权重的近似程度来寻找最佳拓扑结构的方法。

本文提出了一种稀疏级联架构，它是几个稀疏矩阵的乘法，也是一种特定的连接模式，其性能优于其他提供的考虑。


Deep learning models require extensive architecture design exploration and hyperparameter optimization to perform well on a given task.The exploration of the model design space is often made by a human expert, and optimized using a combination of grid search and search heuristics over a large space of possible choices.Neural Architecture Search (NAS) is a Reinforcement Learning approach that has been proposed to automate architecture design.NAS has been successfully applied to generate Neural Networks that rival the best human-designed architectures.However, NAS requires sampling, constructing, and training hundreds to thousands of models to achieve well-performing architectures.This procedure needs to be executed from scratch for each new task.The application of NAS to a wide set of tasks currently lacks a way to transfer generalizable knowledge across tasks.In this paper, we present the Multitask Neural Model Search (MNMS) controller.Our goal is to learn a generalizable framework that can condition model construction on successful model searches for previously seen tasks, thus significantly speeding up the search for new tasks.We demonstrate that MNMS can conduct an automated architecture search for multiple tasks simultaneously while still learning well-performing, specialized models for each task.We then show that pre-trained MNMS controllers can transfer learning to new tasks.By leveraging knowledge from previous searches, we find that pre-trained MNMS models start from a better location in the search space and reduce search time on unseen tasks, while still discovering models that outperform published human-designed models.

我们提出了多任务神经模型搜索，这是一个元学习器，可以同时为多个任务设计模型，并将学习转移到未见过的任务。

本文将神经结构搜索扩展到多任务学习问题上，在这个问题上，学习一个任务条件的模型搜索控制器来同时处理多个任务。

在本文中，作者总结了他们建立一个框架的工作，称为多任务神经模型搜索控制器，用于同时跨多个任务的自动化神经网络构建。


This work studies the problem of modeling non-linear visual processes by leveraging deep generative architectures for learning linear, Gaussian models of observed sequences.We propose a joint learning framework, combining a multivariate autoregressive model and deep convolutional generative networks.After justification of theoretical assumptions of inearization, we propose an architecture that allows Variational Autoencoders and Generative Adversarial Networks to simultaneously learn the non-linear observation as well as the linear state-transition model from a sequence of observed frames.Finally, we demonstrate our approach on conceptual toy examples and dynamic textures.

我们通过生成式深度学习将非线性视觉过程建模为自回归噪音。

提出了一种新的方法，用深度版的线性过程（马尔可夫过程）来模拟非线性的视觉过程。

本文提出了一种新的序列深度生成模型，特别是图像序列和视频，它在模型的一部分使用了线性结构。


Partial differential equations (PDEs)  play a prominent role in many disciplines such as applied mathematics, physics, chemistry, material science, computer science, etc.PDEs are commonly derived based on physical laws or empirical observations.However, the governing equations for many complex systems in modern applications are still not fully known.With the rapid development of sensors, computational power, and data storage in the past decade, huge quantities of data can be easily collected and efficiently stored.Such vast quantity of data offers new opportunities for data-driven discovery of hidden physical laws.Inspired by the latest development of neural network designs in deep learning, we propose a new feed-forward deep network, called PDE-Net, to fulfill two objectives at the same time: to accurately predict dynamics of complex systems and to uncover the underlying hidden PDE models.The basic idea of the proposed PDE-Net is to learn differential operators by learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate the unknown nonlinear responses.Comparing with existing approaches, which either assume the form of the nonlinear response is known or fix certain finite difference approximations of differential operators, our approach has the most flexibility by learning both differential operators and the nonlinear responses.A special feature of the proposed PDE-Net is that all filters are properly constrained, which enables us to easily identify the governing PDE models while still maintaining the expressive and predictive power of the network.These constrains are carefully designed by fully exploiting the relation between the orders of differential operators and the orders of sum rules of filters (an important concept originated from wavelet theory).We also discuss relations of the PDE-Net with some existing networks in computer vision such as Network-In-Network (NIN) and Residual Neural Network (ResNet).Numerical experiments show that the PDE-Net has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.

本文提出了一个新的前馈网络，称为PDE-Net，用于从数据中学习PDEs。

本文阐述了使用深度学习机制来识别由PDEs指定的动态系统的目的。

本文提出了一种基于神经网络的算法，用于从数据中学习，这些数据产生于具有可写成偏微分方程的治理方程的动态系统。

本文通过使用神经架构的非参数偏微分方程来解决复杂的动力系统建模问题，其中最重要的想法是通过papier（PDE-net）来学习微分算子和支配PDE的函数。


Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU parallelism.  However, such simple approximate posteriors are often insufficient, as they eliminate statistical dependencies in the posterior.  While it is possible to use normalizing flow approximate posteriors for continuous latents, there is nothing analogous for discrete latents.The most natural approach to model discrete dependencies is an autoregressive distribution, but sampling from such distributions is inherently sequential and thus slow.  We develop a fast, parallel sampling procedure for autoregressive distributions based on fixed-point iterations which enables efficient and accurate variational inference in discrete state-space models.  To optimize the variational bound, we considered two ways to evaluate probabilities: inserting the relaxed samples directly into the pmf for the discrete distribution, or converting to continuous logistic latent variables and interpreting the K-step fixed-point iterations as a normalizing flow.  We found that converting to continuous latent variables gave considerable additional scope for mismatch between the true and approximate posteriors, which resulted in biased inferences, we thus used the former approach.  We tested our approach on the neuroscience problem of inferring discrete spiking activity from noisy calcium-imaging data, and found that it gave accurate connectivity estimates in an order of magnitude less time.

我们给出了一个类似于正规化流的快速抽样程序，用于离散潜变量模型。

本文通过使用固定点迭代，将自回归滤波变异近似法用于离散动力系统的参数估计。

作者设想了一个针对离散变量或其连续松弛的一般自回归后验家族。

本文有两个主要贡献：它将正规化流程扩展到离散设置，并提出了一个近似的自回归时间序列的定点更新规则，可以利用GPU的并行性。


Deep neural networks (DNNs) had great success on NLP tasks such as language modeling, machine translation and certain question answering (QA) tasks.However, the success is limited at more knowledge intensive tasks such as QA from a big corpus.Existing end-to-end deep QA models (Miller et al., 2016; Weston et al., 2014) need to read the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size.This is prohibitive for practical tasks such as QA from Wikipedia, a novel, or the Web.We propose to solve this scalability issue by using symbolic meaning representations, which can be indexed and retrieved efficiently with complexity that is independent of the text size.More specifically, we use sequence-to-sequence models to encode knowledge symbolically and generate programs to answer questions from the encoded knowledge.We apply our approach, called the N-Gram Machine (NGM), to the bAbI tasks (Weston et al., 2015) and a special version of them (â€œlife-long bAbIâ€) which has stories of up to 10 million sentences.Our experiments show that NGM can successfully solve both of these tasks accurately and efficiently.Unlike fully differentiable memory models, NGMâ€™s time complexity and answering quality are not affected by the story length.The whole system of NGM is trained end-to-end with REINFORCE (Williams, 1992).To avoid high variance in gradient estimation, which is typical in discrete latent variable models, we use beam search instead of sampling.To tackle the exponentially large search space, we use a stabilized auto-encoding objective and a structure tweak procedure to iteratively reduce and refine the search space.

我们提出了一个框架，可以学习对知识进行符号化编码，并生成程序对编码的知识进行推理。

作者提出了N-Gram机器来回答长文档的问题。

本文介绍了n-gram机器，这是一个将句子编码为简单符号表示的模型，可以有效地进行查询。


We propose to use a meta-learning objective that maximizes the speed of transfer on a modified distribution to learn how to modularize acquired knowledge.In particular, we focus on how to factor a joint distribution into appropriate conditionals, consistent with the causal directions.We explain when this can work, using the assumption that the changes in distributions are localized (e.g. to one of the marginals, for example due to an intervention on one of the variables).We prove that under this assumption of localized changes in causal mechanisms, the correct causal graph will tend to have only a few of its parameters with non-zero gradient, i.e. that need to be adapted (those of the modified variables).We argue and observe experimentally that this leads to faster adaptation, and use this property to define a meta-learning surrogate score which, in addition to a continuous parametrization of graphs, would favour correct causal graphs.Finally, motivated by the AI agent point of view (e.g. of a robot discovering its environment autonomously), we consider how the same objective can discover the causal variables themselves, as a transformation of observed low-level variables with no causal meaning.Experiments in the two-variable case validate the proposed ideas and theoretical results.

本文提出了一个基于对转移分布的适应速度的元学习目标，以发现模块化分解和因果变量。

本文表明，具有正确基础结构的模型将比具有不正确结构的模型更快地适应因果干预。

在这项工作中，作者提出了一个通用的、系统的元转移目标框架，其中包含了未知干预下的因果结构学习。


Continual learning is a longstanding goal of artificial intelligence, but is often counfounded by catastrophic forgetting that prevents neural networks from learning tasks sequentially.Previous methods in continual learning have demonstrated how to mitigate catastrophic forgetting, and learn new tasks while retaining performance on the previous tasks.We analyze catastrophic forgetting from the perspective of change in classifier likelihood and propose a simple L1 minimization criterion which can be adapted to different use cases.We further investigate two ways to minimize forgetting as quantified by this criterion and propose strategies to achieve finer control over forgetting.Finally, we evaluate our strategies on 3 datasets of varying difficulty and demonstrate improvements over previously known L2 strategies for mitigating catastrophic forgetting.

关于灾难性遗忘的另一个视角

本文介绍了一个对抗灾难性遗忘的框架，该框架基于改变损失项以最小化分类器可能性的变化，通过泰勒序列近似法获得。

本文试图通过关注正则化方法来解决持续学习的难题，并提出了一种L_1策略来缓解这一问题。


We propose an approach to construct realistic 3D facial morphable models (3DMM) that allows an intuitive facial attributeediting workflow.Current face modeling methods using 3DMM suffer from the lack of local control.We thus create a 3DMM bycombining local part-based 3DMM for the eyes, nose, mouth, ears, and facial mask regions.Our local PCA-based approachuses a novel method to select the best eigenvectors from the local 3DMM to ensure that the combined 3DMM is expressivewhile allowing accurate reconstruction.The editing controls we provide to the user are intuitive as they are extracted fromanthropometric measurements found in the literature.Out of a large set of possible anthropometric measurements, we filter theones that have meaningful generative power given the face data set.We bind the measurements to the part-based 3DMM throughmapping matrices derived from our data set of facial scans.Our part-based 3DMM is compact yet accurate, and compared toother 3DMM methods, it provides a new trade-off between local and global control.We tested our approach on a data set of 135scans used to derive the 3DMM, plus 19 scans that served for validation.The results show that our part-based 3DMM approachhas excellent generative properties and allows intuitive local control to the user.

我们提出了一种构建逼真的三维面部可变形模型（3DMM）的方法，通过选择最佳的特征向量和人体测量集，实现直观的面部属性编辑工作流程。

提出了人脸网格的分片可变形模型，还提出了人脸的人体测量值和模型参数之间的映射，以便合成和编辑具有所需属性的人脸。

本文描述了一种基于部件的可变形面部模型的方法，允许区域的用户控制。


We review eight machine learning classification algorithms to analyze Electroencephalographic (EEG) signals in order to distinguish EEG patterns associated with five basic educational tasks.There is a large variety of classifiers being used in this EEG-based Brain-Computer Interface (BCI) field.While previous EEG experiments used several classifiers in the same experiments or reviewed different algorithms on datasets from different experiments, our approach focuses on review eight classifier categories on the same dataset, including linear classifiers, non-linear Bayesian classifiers, nearest neighbour classifiers, ensemble methods, adaptive classifiers, tensor classifiers, transfer learning and deep learning.Besides, we intend to find an approach which can run smoothly on the current mainstream personal computers and smartphones.  The empirical evaluation demonstrated that Random Forest and LSTM (Long Short-Term Memory) outperform other approaches.We used a data set which users were conducting five frequently-conduct learning-related tasks, including reading, writing, and typing.Results showed that these best two algorithms could correctly classify different users with an accuracy increase of  5% to 9%, use each task independently.Within each subject, the tasks could be recognized with an accuracy increase of  4% to 7%, compared with other approaches.This work suggests that Random Forest could be a recommended approach (fast and accurate) for current mainstream hardware, while LSTM has the potential to be the first-choice approach when the mainstream computers and smartphones can process more data in a shorter time.

在基于EEG的BCI实验中，两种算法的表现优于其他八种算法


Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems.In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction.We introduce two communication protocols - one grounded in the semantics of the game, and one which is a priori ungrounded.  We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded, cheap talk channel to do the same.  However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge.We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.

我们只用强化学习来教代理进行谈判；自私的代理可以这样做，但只能使用值得信赖的沟通渠道，而利社会的代理可以使用随意的谈话进行谈判。

作者描述了一个谈判游戏的变体，考虑了随意的谈话的二级沟通渠道，发现二级渠道改善了谈判的结果。

本文探讨了代理人如何学习交流以解决谈判任务，并发现利社会的代理人能够利用RL学习接地符号，但自利的代理人却不能。

考察了代理人如何在一个简单的谈判游戏中利用沟通来使他们的回报最大化的问题。


The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task.Yet, even with such meta-learning, the low-data problem in the novel classification task still remains.In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data.TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner.  We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.

我们提出了一个新颖的元学习框架，用于归纳推理，一次性对整个测试集进行分类以缓解少量数据问题。

本文提出通过端到端学习标签传播模型的方式来解决少量样本学习的问题，这是第一个学习标签传播的少量样本学习，并产生了有效的经验结果。

本文提出了一个元学习框架，通过以端到端的方式学习基于图的标签传播，来利用未标记的数据。

研究在过渡环境下的少数宿主学习：使用元学习来学习将标签从训练样本传播到测试样本。


We describe the use of an automated scheduling system for observation policy design and to schedule operations of the NASA (National Aeronautics and Space Administration) ECOSystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS).We describe the adaptation of the Compressed Large-scale Activity Scheduler and Planner (CLASP) scheduling system to the ECOSTRESS scheduling problem, highlighting multiple use cases for automated scheduling and several challenges for the scheduling technology: handling long-term campaigns with changing information, Mass Storage Unit Ring Buffer operations challenges, and orbit uncertainty.The described scheduling system has been used for operations of the ECOSTRESS instrument since its nominal operations start July 2018 and is expected to operate until mission end in Summer 2019.

我们描述了使用自动调度系统进行观测策略设计和安排NASA的ECOSTRESS任务的操作。

本文介绍了对自动调度系统CLASP的调整，以ISS上的一个EO实验（ECOSTRESS）为目标。


Adversarial examples are modified samples that preserve original image structures but deviate classifiers.Researchers have put efforts into developing methods for generating adversarial examples and finding out origins.Past research put much attention on decision boundary changes caused by these methods.This paper, in contrast, discusses the origin of adversarial examples from a more underlying knowledge representation point of view.Human beings can learn and classify prototypes as well as transformations of objects.While neural networks store learned knowledge in a more hybrid way of combining all prototypes and transformations as a whole distribution.Hybrid storage may lead to lower distances between different classes so that small modifications can mislead the classifier.A one-step distribution imitation method is designed to imitate distribution of the nearest different class neighbor.Experiments show that simply by imitating distributions from a training set without any knowledge of the classifier can still lead to obvious impacts on classification results from deep networks.It also implies that adversarial examples can be in more forms than small perturbations.Potential ways of alleviating adversarial examples are discussed from the representation point of view.The first path is to change the encoding of data sent to the training step.Training data that are more prototypical can help seize more robust and accurate structural knowledge.The second path requires constructing learning frameworks with improved representations.

对所学知识的混合性存储和表示可能是对抗性例子的一个原因。


Differently from the popular Deep Q-Network (DQN) learning, Alternating Q-learning (AltQ) does not fully fit a target Q-function at each iteration, and is generally known to be unstable and inefficient.Limited applications of AltQ mostly rely on substantially altering the algorithm architecture in order to improve its performance.Although Adam appears to be a natural solution, its performance in AltQ has rarely been studied before.In this paper, we first provide a solid exploration on how well AltQ performs with Adam.We then take a further step to improve the implementation by adopting the technique of parameter restart.More specifically, the proposed algorithms are tested on a batch of Atari 2600 games and exhibit superior performance than the DQN learning method.The convergence rate of the slightly modified version of the proposed algorithms is characterized under the linear function approximation.To the best of our knowledge, this is the first theoretical study on the Adam-type algorithms in Q-learning.

基于亚当的Q-Learning的新实验和理论

本文提供了传统的Q-learning与线性函数近似的收敛结果，当使用类似亚当的更新时。

本文介绍了一种通过使用亚当优化器和定期重启亚当优化器的内部参数相结合的方法来改进AltQ算法。


In search for more accurate predictive models, we customize capsule networks for the learning to diagnose problem.We also propose Spectral Capsule Networks, a novel variation of capsule networks, that converge faster than capsule network with EM routing.Spectral capsule networks  consist of spatial coincidence filters that detect entities based on the alignment of extracted features on a one-dimensional linear subspace.Experiments on a public benchmark learning to diagnose dataset not only shows the success of capsule networks on this task, but also confirm the faster convergence of the spectral capsule networks.

一个新的胶囊网络，在我们的医疗保健基准实验中收敛得更快。

提出了一种胶囊网络的变体，它不使用EM路由，而是采用由前一个胶囊的加权投票矩阵上的主导特征向量所跨越的线性子空间。

本文提出了一种改进的路由方法，它采用了eigendecomposition的工具来寻找胶囊的激活和姿势。


One of the big challenges in machine learning applications is that training data can be different from the real-world data faced by the algorithm.In language modeling, usersâ€™ language (e.g. in private messaging) could change in a year and be completely different from what we observe in publicly available data.At the same time, public data can be used for obtaining general knowledge (i.e. general model of English).We study approaches to distributed fine-tuning of a general model on user private data with the additional requirements of maintaining the quality on the general data and minimization of communication costs.We propose a novel technique that significantly improves prediction quality on usersâ€™ language compared to a general model and outperforms gradient compression methods in terms of communication efficiency.The proposed procedure is fast and leads to an almost 70% perplexity reduction and 8.7 percentage point improvement in keystroke saving rate on informal English texts.Finally, we propose an experimental framework for evaluating differential privacy of distributed training of language models and show that our approach has good privacy guarantees.

我们提出了一种在用户设备上对语言模型进行分布式微调的方法，无需收集私人数据

本文通过采用用户特定文本和一般英语之间的线性插值目标，在移动设备上改进基于用户输入的一小部分文本的语言模型。


We propose that approximate Bayesian algorithms should optimize a new criterion, directly derived from the loss, to calculate their approximate posterior which we refer to as pseudo-posterior.Unlike standard variational inference which optimizes a lower bound on the log marginal likelihood, the new algorithms can be analyzed to provide loss guarantees on the predictions with the pseudo-posterior.Our criterion can be used to derive new sparse Gaussian process algorithms that have error guarantees applicable to various likelihoods.

本文利用对有界假设空间上的Lipschitz损失的分析，推导出具有强大性能保证的新ERM型算法，可应用于非共轭稀疏GP模型。


In this paper, we propose a novel regularization method, RotationOut, for neural networks. Different from Dropout that handles each neuron/channel independently, RotationOut regards its input layer as an entire vector and introduces regularization by randomly rotating the vector. RotationOut can also be used in convolutional layers and recurrent layers with a small modification.We further use a noise analysis method to interpret the difference between RotationOut and Dropout in co-adaptation reduction. Using this method, we also show how to use RotationOut/Dropout together with Batch Normalization. Extensive experiments in vision and language tasks are conducted to show the effectiveness of the proposed method. Codes will be available.

我们提出了一种神经网络的正则化方法和一种噪音分析方法

本文提出了一种新的正则化方法来缓解深度神经网络的过拟合问题，即用随机旋转矩阵旋转特征以减少共适应。

本文提出了一种新的训练神经网络的正则化方法，它以一种相互依赖的方式增加噪音神经元。


Formulating the reinforcement learning (RL) problem in the framework of probabilistic inference not only offers a new perspective about RL, but also yields practical algorithms that are more robust and easier to train.While this connection between RL and probabilistic inference has been extensively studied in the single-agent setting, it has not yet been fully understood in the multi-agent setup.In this paper, we pose the problem of multi-agent reinforcement learning as the problem of performing inference in a particular graphical model.We model the environment, as seen by each of the agents, using separate but related Markov decision processes.We derive a practical off-policy maximum-entropy actor-critic algorithm that we call Multi-agent Soft Actor-Critic (MA-SAC) for performing approximate inference in the proposed model using variational inference.MA-SAC can be employed in both cooperative and competitive settings.Through experiments, we demonstrate that MA-SAC outperforms a strong baseline on several multi-agent scenarios.While MA-SAC is one resultant multi-agent RL algorithm that can be derived from the proposed probabilistic framework, our work provides a unified view of maximum-entropy algorithms in the multi-agent setting.

多代理强化学习的概率框架

本文在非政策性最大熵行为者批评算法软行为者批评（SAC）的基础上，提出了一种名为多行为者软行为者批评（MA-SAC）的新算法。


Sorting input objects is an important step in many machine learning pipelines.However, the sorting operator is non-differentiable with respect to its inputs, which prohibits end-to-end gradient-based optimization.In this work, we propose NeuralSort, a general-purpose continuous relaxation of the output of the sorting operator from permutation matrices to the set of unimodal row-stochastic matrices, where every row sums to one and has a distinct argmax.This relaxation permits straight-through optimization of any computational graph involve a sorting operation.Further, we use this relaxation to enable gradient-based stochastic optimization over the combinatorially large space of permutations by deriving a reparameterized gradient estimator for the Plackett-Luce family of distributions over permutations.We demonstrate the usefulness of our framework on three tasks that require learning semantic orderings of high-dimensional objects, including a fully differentiable, parameterized extension of the k-nearest neighbors algorithm

我们为排序算子提供了一个连续的松弛，实现了端到端的、基于梯度的随机优化。

本文考虑了如何在不明确一定要学习其实际含义或价值的情况下对一些项目进行排序，并提出了一种通过连续松弛进行优化的方法。

这项工作建立在sum(top k)特性的基础上，推导出 "单模行随机 "矩阵的路径可微调采样器。

介绍了排序算子的连续松弛，以构建基于端到端的梯度优化，并介绍了其使用Placket-Luce分布和蒙特卡洛的方法的随机扩展。


Transferring knowledge across tasks to improve data-efficiency is one ofthe open key challenges in the area of global optimization algorithms.Readilyavailable algorithms are typically designed to be universal optimizers and, thus,often suboptimal for specific tasks.We propose a novel transfer learning method toobtain customized optimizers within the well-established framework of Bayesianoptimization, allowing our algorithm to utilize the proven generalizationcapabilities of Gaussian processes.Using reinforcement learning to meta-train anacquisition function (AF) on a set of related tasks, the proposed method learns toextract implicit structural information and to exploit it for improved data-efficiency.We present experiments on a sim-to-real transfer task as well as on several simulatedfunctions and two hyperparameter search problems.The results show that ouralgorithm (1) automatically identifies structural properties of objective functionsfrom available source tasks or simulations, (2) performs favourably in settings withboth scarse and abundant source data, and (3) falls back to the performance levelof general AFs if no structure is present.

我们通过元学习的神经获取函数，在贝叶斯优化的框架内进行高效灵活的转移学习。

作者提出了MetaBO，它使用强化学习来元学习贝叶斯优化的获取函数，显示了在新任务上不断提高的样本效率。

作者提出了一种基于元学习的标准采集函数（AFs）的替代方法，即预训练的神经网络输出采集值作为手选特征的函数。


We study the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the information bottleneck theory.The theory suggests that DNN training comprises a rapid fitting phase followed by a slower compression phase, in which the mutual information I(X;T) between the input X and internal representations T decreases.Several papers observe compression of estimated mutual information on different DNN models, but the true I(X;T) over these networks is provably either constant (discrete X) or infinite (continuous X).This work explains the discrepancy between theory and experiments, and clarifies what was actually measured by these past works.To this end, we introduce an auxiliary (noisy) DNN framework for which I(X;T) is a meaningful quantity that depends on the network's parameters.This noisy framework is shown to be a good proxy for the original (deterministic) DNN both in terms of performance and the learned representations.We then develop a rigorous estimator for I(X;T) in noisy DNNs and observe compression in various models.By relating I(X;T) in the noisy DNN to an information-theoretic communication problem, we show that compression is driven by the progressive clustering of hidden representations of inputs from the same class.Several methods to directly monitor clustering of hidden representations, both in noisy and deterministic DNNs, are used to show that meaningful clusters form in the T space.Finally, we return to the estimator of I(X;T) employed in past works, and demonstrate that while it fails to capture the true (vacuous) mutual information, it does serve as a measure for clustering.This clarifies the past observations of compression and isolates the geometric clustering of hidden representations as the true phenomenon of interest.

确定性的深层神经网络不会丢弃信息，但它们会对输入进行分群。

本文提供了一种原则性的方法来研究深度神经网络中的压缩短语，通过提供一个理论上的听觉熵估计器来估计相互信息。


A central challenge in multi-agent reinforcement learning is the induction of coordination between agents of a team.In this work, we investigate how to promote inter-agent coordination using policy regularization and discuss two possible avenues respectively based on inter-agent modelling and synchronized sub-policy selection.We test each approach in four challenging continuous control tasks with sparse rewards and compare them against three baselines including MADDPG, a state-of-the-art multi-agent reinforcement learning algorithm.To ensure a fair comparison, we rely on a thorough hyper-parameter selection and training methodology that allows a fixed hyper-parameter search budget for each algorithm and environment.We consequently assess both the hyper-parameter sensitivity, sample-efficiency and asymptotic performance of each learning method.Our experiments show that the proposed methods lead to significant improvements on cooperative problems.We further analyse the effects of the proposed regularizations on the behaviors learned by the agents.

我们提出了促进合作任务协调的多代理 RL算法的正规化目标。

本文提出了两种使代理人偏向于学习协调行为的方法，并在具有适当复杂性的多代理人领域对这两种方法进行了严格的评估。

本文在MADDPG的基础上提出了两种方法来鼓励分散的MARL代理之间的协作。


Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities.The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities.However, existing work learns joint representations using multiple modalities as input and may be sensitive to noisy or missing modalities at test time.With the recent success of sequence to sequence models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time.In this paper, we propose a method to learn robust joint representations by translating between modalities.Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input.We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities.Once our translation model is trained with paired multimodal data, we only need data from the source modality at test-time for prediction.This ensures that our model remains robust from perturbations or missing target modalities.We train our model with a coupled translation-prediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICT-MMMO, and YouTube.Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to perturbations of all other modalities.

我们提出了一个模型，该模型通过在多种模式之间进行分层的循环翻译来学习稳定的联合表征。

本文介绍了多模态循环翻译网络（MCTN），并对其用于多模态情感分析进行了评估。


The geometric properties of loss surfaces, such as the local flatness of a solution, are associated with generalization in deep learning.The Hessian is often used to understand these geometric properties.We investigate the differences between the eigenvalues of the neural network Hessian evaluated over the empirical dataset, the Empirical Hessian, and the eigenvalues of the Hessian under the data generating distribution, which we term the True Hessian.Under mild assumptions, we use random matrix theory to show that the True Hessian has eigenvalues of smaller absolute value than the Empirical Hessian.We support these results for different SGD schedules on both a 110-Layer ResNet and VGG-16.To perform these experiments we propose a framework for spectral visualization, based on GPU accelerated stochastic Lanczos quadrature.This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.

了解数据生成分布下的神经网络Hessian特征值。

本文分析了大型神经网络Hessian矩阵的频谱，对最大/最小特征值进行了分析，并使用Lanczos正交方法对频谱进行了视觉化。

本文利用随机矩阵理论研究了深度学习的经验Hessian和真实Hessian的频谱分布，并提出了一种高效的频谱视觉化方法。


Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input.Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text.In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.

改善序列模型的一个简单技巧。将它们与一个图模型组合起来

本文提出了一个结构性总结模型，该模型是由RNN扩展而来的基于图形的编码器。

这项工作将图谱神经网络与抽象化总结的顺序方法相结合，在所有数据集上与外部基线相比都很有效。


In probabilistic classification, a discriminative model based on Gaussian mixture exhibits flexible fitting capability.Nevertheless, it is difficult to determine the number of components.We propose a sparse classifier based on a discriminative Gaussian mixture model (GMM), which is named sparse discriminative Gaussian mixture (SDGM).In the SDGM, a GMM-based discriminative model is trained by sparse Bayesian learning.This learning algorithm improves the generalization capability by obtaining a sparse solution and automatically determines the number of components by removing redundant components.The SDGM can be embedded into neural networks (NNs) such as convolutional NNs and can be trained in an end-to-end manner.Experimental results indicated that the proposed method prevented overfitting by obtaining sparsity.Furthermore, we demonstrated that the proposed method outperformed a fully connected layer with the softmax function in certain cases when it was used as the last layer of a deep NN.

一个基于判别性高斯混合模型的稀疏分类器，它也可以被嵌入到神经网络中。

本文提出了一个通过梯度下降论证训练的高斯混合模型，它可以诱导稀疏性并减少可训练的模型层参数。

本文提出了一个分类器，称为SDGM，基于判别性高斯混合物及其稀疏参数的估计。


We recently observed that convolutional filters initializedfarthest apart from each other using offthe-shelf pre-computed Grassmannian subspacepacking codebooks performed surprisingly wellacross many datasets.Through this short paper,weâ€™d like to disseminate some initial results in thisregard in the hope that we stimulate the curiosityof the deep-learning community towards consideringclassical Grassmannian subspace packingresults as a source of new ideas for more efficientinitialization strategies.

使用现成的Grassmannian编码簿初始化权重，获得更快的训练和更好的准确性


Domain adaptation is critical for success in new, unseen environments.Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts.Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at  mapping images between domains, even without the use of aligned image pairs.We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model.CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs.  Our model can be applied in a variety of visual recognition and prediction settings.We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.

一种无监督的领域适应方法，在像素和特征层面上都能适应。

本文提出了一种领域适应的方法，通过扩展CycleGAN的任务特定损失函数和对像素和特征施加的损失。

本文提出了使用CycleGANs进行领域适应的建议

本文通过将CycleGAN与对抗性适应方法耦合在一起，在CycleGAN的总体目标中包括一个新的特征和语义损失，对以前的工作做了一个新的扩展，有明显的好处。


Stemming is the process of removing affixes( i.e. prefixes, infixes and suffixes) that improve the accuracy and performance of information retrieval systems.This paper presents the reduction of Amharic words to corresponding stem where with the intention that it preserves semantic information.The proposed approach efficiently removes affixes from an Amharic word.The process of removing such affixes (prefixes, infixes and suffixes) from a word to its base form is called stemming.While many stemmers exist for dominant languages such as English, under resourced languages such as Amharic which lacks such powerful tool support.In this paper, we design a light Amharic stemmer relying on the rules that receives an Amharic word and then it finds a match to the beginning of a word to the possible prefixes and to its ending with the possible suffixes and finally it checks whether it has infix.The final result is the stem if there is any prefix, infix or/and suffix, otherwise it remains in one of the earlier states.The technique does not rely on any additional resource (e.g. dictionary) to verify the generated stem.The performance of the generated stemmer is evaluated using manually annotated Amharic words.The result is compared with current state-of-the-art stemmer for Amharic showing an increase of 7% in stemmer correctness.

Amharic Light Stemmer是为提高Amharic Sentiment Classification的性能而设计的。

本文研究了对形态丰富的语言进行词干提取的问题，采用的是词干提取器，只在保留词中原始语义信息的前提下去除词缀。

本文提出了一种阿姆哈拉语的轻度词干提取，使用一连串的转换，使形式标准化，去除后缀、前缀和下缀。


Place and grid-cells are known to aid navigation in animals and humans.Together with concept cells, they allow humans to form an internal representation of the external world, namely the concept space.We investigate the presence of such a space in deep neural networks by plotting the activation profile of its hidden layer neurons.Although place cell and concept-cell like properties are found, grid-cell like firing patterns are absent thereby indicating a lack of path integration or feature transformation functionality in trained networks.Overall, we present a plausible inadequacy in current deep learning practices that restrict deep networks from performing analogical reasoning and memory retrieval tasks.

我们研究了简单的深度网络是否拥有类似网格细胞的人工神经元，同时在学习的概念空间中进行记忆检索。


We develop a comprehensive description of the active inference framework, as proposed by Friston (2010), under a machine-learning compliant perspective.Stemming from a biological inspiration and the auto-encoding principles, a sketch of a cognitive architecture is proposed that should provide ways to implement estimation-oriented control policies.  Computer simulations illustrate the effectiveness of the approach through a foveated inspection of the input data.The pros and cons of the control policy are analyzed in detail, showing interesting promises in terms of processing compression.Though optimizing future posterior entropy over the actions set is shown enough to attain locally optimal action selection, offline calculation using class-specific saliency maps is shown better for it saves processing costs through saccades pathways pre-processing, with a negligible effect on the recognition/compression rates.

预测性编码角度下基于囊状体的电脑视觉的利与弊

提出了一个主动视觉问题的计算框架，并解释了如何学习控制策略以减少后验信念的熵值。


Graphs possess exotic features like variable size and absence of natural ordering of the nodes that make them difficult to analyze and compare.To circumvent this problem and learn on graphs, graph feature representation is required.Main difficulties with feature extraction lie in the trade-off between expressiveness, consistency and efficiency, i.e. the capacity to extract features that represent the structural information of the graph while being deformation-consistent and isomorphism-invariant.While state-of-the-art methods enhance expressiveness with powerful graph neural-networks, we propose to leverage natural spectral properties of graphs to study a simple graph feature: the graph Laplacian spectrum (GLS).We analyze the representational power of this object that satisfies both isomorphism-invariance, expressiveness and deformation-consistency.In particular, we propose a theoretical analysis based on graph perturbation to understand what kind of comparison between graphs we do when comparing GLS.To do so, we derive bounds for the distance between GLS that are related to the divergence to isomorphism, a standard computationally expensive graph divergence.Finally, we experiment GLS as graph representation through consistency tests and classification tasks, and show that it is a strong graph feature representation baseline.

我们从理论上研究了拉普拉斯频谱的一致性，并将其作为全图嵌入的方法。 本文以图的拉普拉斯频谱为手段，生成一种用于比较图和分类的表示方法。 这项工作提出使用图拉普拉斯频谱来学习图的表示。


Adversarial training, a method for learning robust deep networks, is typically assumed to be more expensive than traditional training due to the necessity of constructing adversarial examples via a first-order method like projected gradient decent (PGD).  In this paper, we make the surprising discovery that it is possible to train empirically robust models using a much weaker and cheaper adversary, an approach that was previously believed to be ineffective, rendering the method no more costly than standard training in practice.  Specifically, we show that adversarial training with the fast gradient sign method (FGSM), when combined with random initialization, is as effective as PGD-based training but has significantly lower cost.  Furthermore we show that FGSM adversarial training can be further accelerated by using standard techniques for efficient training of deep networks, allowing us to learn a robust CIFAR10 classifier with 45% robust accuracy at epsilon=8/255 in 6 minutes, and a robust ImageNet classifier with 43% robust accuracy at epsilon=2/255 in 12 hours, in comparison to past work based on ``free'' adversarial training which took 10 and 50 hours to reach the same respective thresholds.

基于FGSM的对抗性训练，加上随机化，和基于PGD的对抗性训练一样好用：我们可以用它在CIFAR10上训练一个稳定的分类器，只需6分钟，在ImageNet上训练12小时，在单台机器上。

本文重新审视了随机+FGSM方法，以比以前的方法更快的速度训练针对强PGD逃避攻击的稳健模型。

本文的主要主张是，随机化加快速梯度符号法（FGSM）对抗性训练的简单策略可以产生稳定的神经网络。


In seeking for sparse and efficient neural network models, many previous works investigated on enforcing L1 or L0 regularizers to encourage weight sparsity during training.The L0 regularizer measures the parameter sparsity directly and is invariant to the scaling of parameter values.But it cannot provide useful gradients and therefore requires complex optimization techniques.The L1 regularizer is almost everywhere differentiable and can be easily optimized with gradient descent.Yet it is not scale-invariant and causes the same shrinking rate to all parameters, which is inefficient in increasing sparsity.Inspired by the Hoyer measure (the ratio between L1 and L2 norms) used in traditional compressed sensing problems, we present DeepHoyer, a set of sparsity-inducing regularizers that are both differentiable almost everywhere and scale-invariant.Our experiments show that enforcing DeepHoyer regularizers can produce even sparser neural network models than previous works, under the same accuracy level.We also show that DeepHoyer can be applied to both element-wise and structural pruning.

我们为DNN的修剪提出了几乎无处不在的可微分和尺度不变的正则器，它可以通过标准的SGD训练来实现超值的稀疏性。

本文提出了一个受Hoyer度量启发的尺度不变量正则器（DeepHoyer），以强制执行神经网络的稀疏性。


Self-supervision, in which a target task is improved without external supervision, has primarily been explored in settings that assume the availability of additional data.However, in many cases, particularly in healthcare, one may not have access to additional data (labeled or otherwise).In such settings, we hypothesize that self-supervision based solely on the structure of the data at-hand can help.We explore a novel self-supervision framework for time-series data, in which multiple auxiliary tasks (e.g., forecasting) are included to improve overall performance on a sequence-level target task without additional training data.We call this approach limited self-supervision, as we limit ourselves to only the data at-hand.We demonstrate the utility of limited self-supervision on three sequence-level classification tasks, two pertaining to real clinical data and one using synthetic data.Within this framework, we introduce novel forms of self-supervision and demonstrate their utility in improving performance on the target task.Our results indicate that limited self-supervision leads to a consistent improvement over a supervised baseline, across a range of domains.In particular, for the task of identifying atrial fibrillation from small amounts of electrocardiogram data, we observe a nearly 13% improvement in the area under the receiver operating characteristics curve (AUC-ROC) relative to the baseline (AUC-ROC=0.55 vs. AUC-ROC=0.62).Limited self-supervision applied to sequential data can aid in learning intermediate representations, making it particularly applicable in settings where data collection is difficult.

我们表明，自监督的辅助任务要想对时间序列分类有用，并不需要额外的未标记数据，并提出了新的有效的辅助任务。

本文提出了一种自监督的方法，通过设计基于数据内部结构的辅助任务来创建更多的标签化辅助训练任务，从而从医疗环境中的时间序列数据中学习。

本文提出了一种对时间序列进行自我监督学习的方法。


Are neural networks biased toward simple functions?Does depth always help learn more complex features?Is training the last layer of a network as good as training all layers?These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective.We will study the spectra of the *Conjugate Kernel, CK,* (also called the *Neural Network-Gaussian Process Kernel*), and the *Neural Tangent Kernel, NTK*.Roughly, the CK and the NTK tell us respectively ``"what a network looks like at initialization" and "``what a network looks like during and after training."Their spectra then encode valuable information about the initial distribution and the training and generalization properties of neural networks.By analyzing the eigenvalues, we lend novel insights into the questions put forth at the beginning, and we verify these insights by extensive experiments of neural networks.We believe the computational tools we develop here for analyzing the spectra of CK and NTK serve as a solid foundation for future studies of deep neural networks.We have open-sourced the code for it and for generating the plots in this paper at github.com/jxVmnLgedVwv6mNcGCBy/NNspectra.

共轭（又称NNGP）和神经正切核的特征值可以在布林立方体上以封闭形式计算，并揭示了超参数对神经网络归纳偏差、训练和概括的影响。

本文对神经网络的共轭核和布林立方体上的神经正切核进行了频谱分析，以解决为什么深度网络偏向于简单函数的问题。


To communicate, to ground hypotheses, to analyse data, neuroscientists often refer to divisions of the brain.Here we consider atlases used to parcellate the brain when studying brain function.We discuss the meaning and the validity of these parcellations, from a conceptual point of view as well as by running various analytical tasks on popular functional brain parcellations.

所有的大脑功能分区都是错误的，但有些是有用的


High-dimensional sparse reward tasks present major challenges for reinforcement learning agents.  In this work we use imitation learning to address two of these challenges:  how to learn a useful representation of the world e.g.  from pixels, and how to explore efficiently given the rarity of a reward signal?We show that adversarial imitation can work well even in this high dimensional observation space.Surprisingly the adversary itself, acting as the learned reward function, can be tiny, comprising as few as 128 parameters, and can be easily trained using the most basic GAN formulation.Our approach removes limitations present in most contemporary imitation approaches: requiring no demonstrator actions (only video), no special initial conditions or warm starts, and no explicit tracking of any single demo.The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.  Furthermore, our agent learns much faster than competing approaches that depend on hand-crafted, staged dense reward functions, and also better compared to standard GAIL baselines.Finally, we develop a new adversarial goal recognizer that in some cases allows the agent to learn stacking without any task reward, purely from imitation.

使用非政策性RL和一个微小的对抗性学习的奖励函数，从像素进行模仿，含稀少奖励或没有奖励。

本文提出在高维视觉空间下的生成式对抗性模仿学习中使用 "最小对抗者"。

本文旨在解决在高维输入环境下估计稀疏奖励的问题。


In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework.One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them.The other strategy learns formal specifications from the real data and shows that fake samples violate the specifications of the real data.We show that fake samples produced with GANs have a universal signature that can be used to identify fake samples.We provide results on MNIST, CIFAR10, music and speech data.

我们展示了轻松识别用生成式对抗网络框架生成的虚假样本的策略。

表明用普通生成对抗网络（GAN）实现的假样本很容易用各种统计技术来识别。

本文提出了基于简单的边际统计或从真实数据中自动生成的形式规范的统计数据来识别使用GANs生成的假数据。


Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence.The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices.This imposes an upper-bound on the reduction of complexity of multiply-accumulate units.We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training.Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation.We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet.In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline.We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds.Overall this analysis enables precise tailoring of computation hardware to the application, yielding area- and power-optimal systems.

我们提出了一个分析框架，以确定所有三个深度学习训练GEMM中的累积位宽要求，并通过基准实验验证我们方法的有效性和严密性。

作者提出了一种分析方法来预测卷积层和全连接层的部分求和所需的尾数位元数

作者对神经网络训练中的累加操作所需的数字精度进行了深入分析，并展示了减少浮点累加器中的位元数所带来的理论影响。


Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain.However, the two predominant methods, domain discrepancy reduction learning and semi-supervised learning, are not readily applicable when source and target domains do not share a common label space.This paper addresses the above scenario by learning a representation space that retains discriminative power on both the (labeled) source and (unlabeled) target domains while keeping representations for the two domains well-separated.Inspired by a theoretical analysis, we first reformulate the disjoint classification task, where the source and target domains correspond to non-overlapping class labels, to a verification one.To handle both within and cross domain verifications, we propose a Feature Transfer Network (FTN) to separate the target feature space from the original source space while aligned with a transformed source space.Moreover, we present a non-parametric multi-class entropy minimization loss to further boost the discriminative power of FTNs on the target domain.In experiments, we first illustrate how FTN works in a controlled setting of adapting from MNIST-M to MNIST with disjoint digit classes between the two domains and then demonstrate the effectiveness of FTNs through state-of-the-art performances on a cross-ethnicity face recognition problem.

一种新的非监督领域适应距离度量学习的理论，并将其应用于不同民族差异的人脸识别。 提出了一种新型的特征转移网络，优化了领域对抗性损失和领域分离性损失。


In this paper, we consider the problem of training neural networks (NN).To promote a NN with specific structures, we explicitly take into consideration the nonsmooth regularization (such as L1-norm) and constraints (such as interval constraint).This is formulated as a constrained nonsmooth nonconvex optimization problem, and we propose a convergent proximal-type stochastic gradient descent (Prox-SGD) algorithm.We show that under properly selected learning rates, momentum eventually resembles the unknown real gradient and thus is crucial in analyzing the convergence.We establish that with probability 1, every limit point of the sequence generated by the proposed Prox-SGD is a stationary point.Then the Prox-SGD is tailored to train a sparse neural network and a binary neural network, and the theoretical analysis is also supported by extensive numerical tests.

我们提出了一种收敛的近似型随机梯度下降算法，用于解决有约束的非光滑非凸面优化问题

本文提出了Prox-SGD，这是一个随机优化算法的理论框架，显示出对于平滑的非凸面损失+凸面约束/正则器来说，渐进地收敛到静止性。

本文通过将近似算法的理论适应于非凸设置，提出了一种新的基于梯度的随机优化算法，具有梯度平均性。


The loss of a few neurons in a brain rarely results in any visible loss of function.However, the insight into what â€œfewâ€ means in this context is unclear.How many random neuron failures will it take to lead to a visible loss of function?In this paper, we address the fundamental question of the impact of the crash of a random subset of neurons on the overall computation of a neural network and the error in the output it produces.We study fault tolerance of neural networks subject to small random neuron/weight crash failures in a probabilistic setting.We give provable guarantees on the robustness of the network to these crashes.Our main contribution is a bound on the error in the output of a network under small random Bernoulli crashes proved by using a Taylor expansion in the continuous limit, where close-by neurons at a layer are similar.The failure mode we adopt in our model is characteristic of neuromorphic hardware, a promising technology to speed up artificial neural networks, as well as of biological networks.We show that our theoretical bounds can be used to compare the fault tolerance of different architectures and to design a regularizer improving the fault tolerance of a given architecture.We design an algorithm achieving fault tolerance using a reasonable number of neurons.In addition to the theoretical proof, we also provide experimental validation of our results and suggest a connection to the generalization capacity problem.

我们给出了一个NN在随机权重失效情况下的输出误差的约束，在附近神经元相似的连续极限中使用了泰勒扩展

本文考虑了从神经网络中丢弃神经元的问题，表明如果目标是在评估过程中对随机丢弃的神经元变得稳定，那么只需进行随机失活训练即可。

这一贡献研究了随机神经元的删除对训练过的架构的预测准确性的影响，并应用于故障分析和神经形态硬件的具体环境。


Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world.In robotics, we have seen tremendous progress in using visual and tactile perception; however we have often ignored a key sense: sound.This is primarily due to lack of data that captures the interplay of action and sound.In this work, we perform the first large-scale study of the interactions between sound and robotic action.To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot.By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information.Using this data, we explore the synergies between sound and action, and present three key insights.First, sound is indicative of fine-grained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench.Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied on the object.Finally, object representations derived from audio embeddings are indicative of implicit physical properties.We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings.

我们探索和研究声音和行动之间的协同作用。

本文通过用倾斜机器人建立一个声音-动作-视觉数据集来探索动作和声音之间的联系。

本文研究音频在物体和动作感知中的作用，以及听觉信息如何帮助学习前向和反向动力学模型。


Hierarchical label structures widely exist in many machine learning tasks, ranging from those with explicit label hierarchies such as image classification to the ones that have latent label hierarchies such as semantic segmentation.Unfortunately, state-of-the-art methods often utilize cross-entropy loss which in-explicitly assumes the independence among class labels.Motivated by the fact that class members from the same hierarchy need to be similar to each others, we design a new training diagram called Hierarchical Complement Objective Training (HCOT).In HCOT, in addition to maximizing the probability of the ground truth class, we also neutralize the probabilities of rest of the classes in a hierarchical fashion, making the model take advantage of the label hierarchy explicitly.We conduct our method on both image classification and semantic segmentation.Results show that HCOT outperforms state-of-the-art models in CIFAR100, Imagenet, and PASCAL-context.Our experiments also demonstrate that HCOT can be applied on tasks with latent label hierarchies, which is a common characteristic in many machine learning tasks.

我们提出了分层互补目标训练，这是一种新的训练范例，可以有效地利用标签空间中的类别层次来进行图像分类和语义分割。

一种使类的后验分布的熵正规化的方法，可用于图像分类和分割任务


There is a growing interest in automated neural architecture search (NAS).To improve the efficiency of NAS, previous approaches adopt  weight sharing method to force all models share the same set of weights.  However, it has been observed that a model performing better with shared weights does not necessarily perform  better when trained alone.In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the posterior fading problem, which compromises the effectiveness of shared weights.To alleviate this problem, we present a practical approach to guide the parameter posterior towards its true distribution.Moreover, a hard latency constraint is introduced during the search so that the desired latency can be achieved.The resulted method, namely Posterior Convergent NAS (PC-NAS), achieves state-of-the-art performance under standard GPU latency constraint on ImageNet.In our small search space, our model PC-NAS-S attains76.8% top-1 accuracy, 2.1% higher than MobileNetV2 (1.4x) with the same latency.When adopted to our large search space, PC-NAS-L achieves 78.1% top-1 accuracy within 11ms.The discovered architecture also transfers well to other computer vision applications such as object detection and person re-identification.

我们的论文确定了现有的神经结构搜索中的权重共享方法的问题，并提出了一个实用的方法，取得了强大的效果。

作者发现了NAS的一个问题，称为后验衰减，并介绍了后验收敛NAS以减轻这种影响。


Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels.In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized.Then, we resume training the early stopped network using a "maximal safe set," which maintains a collection of almost certainly true-labeled samples at each epoch since the early stop point.Putting them all together, our novel two-phase training method, called Prestopping, realizes noise-free training under any type of label noise for practical use.Extensive experiments using four image benchmark data sets verify that our method significantly outperforms four state-of-the-art methods in test error by 0.4â€“8.2 percent points under existence of real-world noise.

我们提出了一种基于 "早期停止 "的新颖的两阶段训练方法，用于在噪音标签上进行稳定的训练。

论文提出研究优化中的早期停止如何帮助找到有把握的例子

本文提出了一种两阶段的训练方法，用于带标签噪音的学习。


Learning when to communicate and doing that effectively is essential in multi-agent tasks.Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks.In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings.IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues.Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases.Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.

我们介绍了IC3Net，它是一个单一的网络，可以用来训练合作、竞争和混合场景下的代理。我们还表明，代理可以使用我们的模型学习何时进行交流。

作者提出了一种新的多代理强化学习架构，该架构使用几个权重相同的LSTM控制器，相互传输一个连续向量。

作者提出了一个有趣的门控方案，允许代理在多代理RL环境中进行通信。


Neural sequence-to-sequence models are a recently proposed family of approaches used in abstractive summarization of text documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text.Despite the advances in abstractive summarization, custom generation of summaries (e.g. towards a user's preference) remains unexplored.In this paper, we present CATS, an abstractive neural summarization model, that summarizes content in a sequence-to-sequence fashion but also introduces a new mechanism to control the underlying latent topic distribution of the produced summaries.Our experimental results on the well-known CNN/DailyMail dataset show that our model achieves state-of-the-art performance.

我们提出了第一个能够定制生成的摘要的神经抽象化模型。


We propose a software framework based on ideas of the Learning-Compression algorithm , that allows one to compress any neural network by different compression mechanisms (pruning, quantization, low-rank, etc.).By design, the learning of the neural net (handled by SGD) is decoupled from the compression of its parameters (handled by a signal compression function), so that the framework can be easily extended to handle different combinations of neural net and compression type.In addition, it has other advantages, such as easy integration with deep learning frameworks, efficient training time, competitive practical performance in the loss-compression tradeoff, and reasonable convergence guarantees.Our toolkit is written in Python and Pytorch and we plan to make it available by the workshop time, and eventually open it for contributions from the community.

我们提出了一个基于学习-压缩算法思想的软件框架，它允许人们通过不同的压缩机制（剪枝、量化、低等级等）来压缩任何神经网络。

本文介绍了一个软件库的设计，通过隐藏压缩方法的细节，使用户更容易压缩他们的网络。


This work seeks the possibility of generating the human face from voice solely based on the audio-visual data without any human-labeled annotations.To this end, we propose a multi-modal learning framework that links the inference stage and generation stage.First, the inference networks are trained to match the speaker identity between the two different modalities.Then the pre-trained inference networks cooperate with the generation network by giving conditional information about the voice.

本文提出了一种基于自监督学习框架的端到端多模态人脸生成方法。

本文提出了一个多模态学习框架，它将推理阶段和生成阶段连结起来，以寻求仅从语音中生成人脸的可能性。

这项工作的目的是建立一个来自音频信号的有条件的人脸图像生成框架。


We present a simple neural model that given a formula and a property tries to answer the question whether the formula has the given property, for example whether a propositional formula is always true.The structure of the formula is captured by a feedforward neural network recursively built for the given formula in a top-down manner.The results of this network are then processed by two recurrent neural networks.One of the interesting aspects of our model is how propositional atoms are treated.For example, the model is insensitive to their names, it only matters whether they are the same or distinct.

提出了一种自上而下的方法，即如何用神经网络循环地表示命题公式。

本文提供了一个新的逻辑公式的神经网络模型，它通过自上而下地遍历解析树来收集关于一个给定公式的信息。

本文追求的是与命题微积分公式的解析树同构的树状结构网络的路径，但通过自上而下而不是自下而上地传递信息。


Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.We identify three key challenges that any algorithm needs to master in order to perform well on all games:  processing diverse reward distributions, reasoning over long time horizons, and exploring efficiently.  In this paper, we propose an algorithm that addresses each of these challenges and is able to learn human-level policies on nearly all Atari games.A new transformed Bellman operator allows our algorithm to process rewards of varying densities and scales; an auxiliary temporal consistency loss allows us to train stably using a discount factor of 0.999 (instead of 0.99) extending the effective planning horizon by an order of magnitude; and we ease the exploration problem by using human demonstrations that guide the agent towards rewarding states.When tested on a set of 42 Atari games, our algorithm exceeds the performance  of an average human on 40 games using a common set of hyper parameters.

Ape-X DQfD = 分布式（许多演员+一个学习者+优先重放）DQN，展示优化了雅达利上未裁减的0.999折的回报。

本文提出了对DQN的三个扩展（贝尔曼更新、时间一致性损失和专家论证），以提高在Atari游戏上的学习性能，在Atari游戏上取得了超过最先进的结果。

本文提出了一个转化的贝尔曼运算子，旨在解决对未裁减奖励的敏感性、对贴现因子值的稳定性以及探索问题。


The knowledge that humans hold about a problem often extends far beyond a set of training data and output labels.While the success of deep learning mostly relies on supervised training, important properties cannot be inferred efficiently from end-to-end annotations alone, for example causal relations or domain-specific invariances.We present a general technique to supplement supervised training with prior knowledge expressed as relations between training instances.We illustrate the method on the task of visual question answering to exploit various auxiliary annotations, including relations of equivalence and of logical entailment between questions.Existing methods to use these annotations, including auxiliary losses and data augmentation, cannot guarantee the strict inclusion of these relations into the model since they require a careful balancing against the end-to-end objective.Our method uses these relations to shape the embedding space of the model, and treats them as strict constraints on its learned representations.%The resulting model encodes relations that better generalize across instances.In the context of VQA, this approach brings significant improvements in accuracy and robustness, in particular over the common practice of incorporating the constraints as a soft regularizer.We also show that incorporating this type of prior knowledge with our method brings consistent improvements, independently from the amount of supervised data used.It demonstrates the value of an additional training signal that is otherwise difficult to extract from end-to-end annotations alone.

在有监督的训练过程中对所学的嵌入进行严格约束的训练方法。应用于视觉问题回答。

作者提出了一个框架，将额外的语义先验知识纳入深度学习模型的传统训练中，使嵌入空间而不是参数空间正规化。

本文主张在多模态神经网络的语言嵌入层中对外部知识进行编码，作为一组硬约束。


Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems.On the other hand, for many problems, classic algorithms exist, which typically exceed the accuracy and stability of neural networks.To combine these two concepts, we present a new kind of neural networksâ€”algorithmic neural networks (AlgoNets).These networks integrate smooth versions of classic algorithms into the topology of neural networks.Our novel reconstructive adversarial network (RAN) enables solving inverse problems without or with only weak supervision.

通过使用平滑近似的前向算法来训练逆向模型来解决逆向问题。


Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain.In this work, we focus on  weakly supervised localization (WSL) where a model is trained to classify an image and localize regions of interest at pixel-level using only global image annotation.Typical convolutional attentions maps are prune to high false positive regions.To alleviate this issue, we propose a new deep learning method for WSL, composed of a localizer and a classifier, where the localizer is constrained to determine relevant and irrelevant regions using conditional entropy (CE) with the aim to reduce false positive regions.Experimental results on a public medical dataset and two natural datasets, using Dice index, show that, compared to state of the art WSL methods, our proposal can provide significant improvements in terms of image-level classification and pixel-level localization (low false positive) with robustness to overfitting.A public reproducible PyTorch implementation is provided.

一种用于弱监督的点状定位的深度学习方法，只使用图像级标签进行学习。它依靠条件熵来定位相关和不相关的区域，旨在最小化假阳性区域。

这项工作利用新颖的正则化项设计和递归擦除算法，探索了WSL的问题。

本文提出了一种新的弱监督方法，用于学习带有图像级别标签的物体分割。


Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency.Particularly, Dyna architecture, as an elegant model-based architecture integrating learning and planning, provides huge flexibility of using a model.One of the most important components in Dyna is called search-control, which refers to the process of generating state or state-action pairs from which we query the model to acquire simulated experiences.Search-control is critical to improve learning efficiency.In this work, we propose a simple and novel search-control strategy by searching high frequency region on value function.Our main intuition is built on Shannon sampling theorem from signal processing, which indicates that a high frequency signal requires more samples to reconstruct.We empirically show that a high frequency function is more difficult to approximate.This suggests a search-control strategy: we should use states in high frequency region of the value function to query the model to acquire more samples.We develop a simple strategy to locally measure the frequency of a function by gradient norm, and provide theoretical justification for this approach.We then apply our strategy to search-control in Dyna, and conduct experiments to show its property and effectiveness on benchmark domains.

从高频区域获取状态，用于Dyna的搜索控制。 作者提议在高频域做采样，以提高采样效率 本文提出了一种在dyna算法中选择做转换的状态的新方法。


We propose a new architecture for distributed image compression from a group of distributed data sources.The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy.The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources.Its compression capability is much better than the method of training codecs separately.Meanwhile, for 10 distributed sources, our distributed system remarkably performs within 2 dB peak signal-to-noise ratio (PSNR) of that of a single codec trained with all data sources.We experiment distributed sources with different correlations and show how our methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC).Our method is also shown to be robust to the lack of presence of encoded data from a number of distributed sources.Moreover, it is scalable in the sense that codes can be decoded simultaneously at more than one compression quality level.To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning.

我们介绍了一个基于分布式循环自动编码器的数据导向的分布式源编码框架，用于可扩展的图像压缩（DRASIC）。

该文提出了一种用于图像压缩的分布式循环自动编码器，它使用ConvLSTM来学习二进制代码，这些代码是由先前编码信息的残余部分逐步构建的。

作者提出了一种在多个源上训练图像压缩模型的方法，每个源上有一个单独的编码器，还有一个共享的解码器。


Long short-term memory networks (LSTMs) were introduced to combat vanishing gradients in simple recurrent neural networks (S-RNNs) by augmenting them with additive recurrent connections controlled by gates.We present an alternate view to explain the success of LSTMs: the gates themselves are powerful recurrent models that provide more representational power than previously appreciated.We do this by showing that the LSTM's gates can be decoupled from the embedded S-RNN, producing a restricted class of RNNs where the main recurrence computes an element-wise weighted sum of context-independent functions of the inputs.Experiments on a range of challenging NLP problems demonstrate that the simplified gate-based models work substantially better than S-RNNs, and often just as well as the original LSTMs, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.

门在LSTM中通过计算元素加权和来完成所有繁重的工作，去除内部的简单RNN并不会降低模型的性能。

本文通过消除内容项和输出门的非线性，提出了一种简化的LSTM变体。

本文对LSTMS进行了分析，表明它们有一种形式，即每一步的存储单元内容是每一步计算的 "内容更新 "值的加权组合，并对LSTM进行了简化，即在每一步计算存储单元的值是输入的确定性函数，而不是输入和当前环境的函数。

本文对LSTM提出了新的见解，其中核心是一个元素加权和，并认为LSTM只保留输入门和遗忘门来计算权重是多余的。


Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision.This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning.In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility we identified.We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data accessibility and code accessibility.  Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.

通过分析近期机器学习会议上的300多篇论文，我们发现，在可重复性指标方面，机器学习促进健康（ML4H）的应用落后于其他机器学习领域。

本文对ML医疗应用的可重制性状况进行了定量和定性的审查，并提出了使研究更可重制的建议。


We propose a solution for evaluation of mathematical expression.However, instead of designing a single end-to-end model we propose a Lego bricks style architecture.In this architecture instead of training a complex end-to-end neural network, many small networks can be trained independently each accomplishing one specific operation and acting a single lego brick.More difficult or complex task can then be solved using a combination of these smaller network.In this work we first identify 8 fundamental operations that are commonly used to solve arithmetic operations (such as 1 digit multiplication, addition, subtraction, sign calculator etc).These fundamental operations are then learned using simple feed forward neural networks.We then shows that different operations can be designed simply by reusing these smaller networks.As an example we reuse these smaller networks to develop larger and a more complex network to solve n-digit multiplication, n-digit division, and cross product.This bottom-up strategy not only introduces reusability, we also show that it allows to generalize for computations involving n-digits and we show results for up to 7 digit numbers.Unlike existing methods, our solution also generalizes for both positive as well as negative numbers.

我们训练许多小的网络，每个网络都针对一个特定的操作，然后将这些网络组合起来，以执行复杂的操作。

本文提出使用神经网络来评估数学表达式，通过设计8个基本运算的小积木，如加法、减法等，然后用这些小积木设计多位数的乘法和除法。

本文提出了一种设计基于NN的数学表达评估引擎的方法。


In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real.The generator is trained to increase the probability that fake data is real.We argue that it should also simultaneously decrease the probability that real data is real because1) this would account for a priori knowledge that half of the data in the mini-batch is fake,2) this would be observed with divergence minimization, and3) in optimal settings, SGAN would be equivalent to integral probability metric (IPM) GANs. We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data.We also present a variant in which the discriminator estimate the probability that the given real data is more realistic than fake data, on average.We generalize both approaches to non-standard GAN loss functions and we refer to them respectively as Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs).We show that IPM-based GANs are a subset of RGANs which use the identity function. Empirically, we observe that1) RGANs and RaGANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts,2) Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and3) RaGANs are able to generate plausible high resolutions images (256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these images are of significantly better quality than the ones generated by WGAN-GP and SGAN with spectral normalization.The code is freely available on https://github.com/AlexiaJM/RelativisticGAN.

使用相对论判别器提高GANs的质量和稳定性；IPM GANs（如WGAN-GP）是一个特例。

本文提出了一个 "相对论判别器"，虽然对超参数、架构和数据集有点敏感，但在某些情况下有帮助。

在这项工作中，作者考虑了GAN的一个变化，即同时降低生成器的真实数据的概率。


Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.However, policy gradients can suffer from large variance that may limit performance, and in practice require carefully tuned entropy regularization to prevent policy collapse.As an alternative to policy gradient algorithms, we introduce V-MPO, an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) that performs policy iteration based on a learned state-value function.We show that V-MPO surpasses previously reported scores for both the Atari-57 and DMLab-30 benchmark suites in the multi-task setting, and does so reliably without importance weighting, entropy regularization, or population-based tuning of hyperparameters.On individual DMLab and Atari levels, the proposed algorithm can achieve scores that are substantially higher than has previously been reported.V-MPO is also applicable to problems with high-dimensional, continuous action spaces, which we demonstrate in the context of learning to control simulated humanoids with 22 degrees of freedom from full state observations and 56 degrees of freedom from pixel observations, as well as example OpenAI Gym tasks where V-MPO achieves substantially higher asymptotic scores than previously reported.

一个基于状态值函数的MPO版本，在离散和连续控制的广泛任务中取得了良好的效果。

本文提出了一种政策性强化学习的算法，该算法可以处理连续/离散控制、单/多任务学习，并同时使用低维状态和像素。

本文提出了MPO的一个在线变体--V-MPO，它可以学习V型函数，并朝着优势更新非参数分布。


Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence.There has been a significant body of work studying neural networks that mimic general computation, but these networks fail to generalize to data distributions that are outside of their training set.We study this problem through the lens of fundamental computer science problems: sorting and graph processing.We modify the masking mechanism of a transformer in order to allow them to implement rudimentary functions with strong generalization.We call this model the Neural Execution Engine, and show that it learns, through supervision, to numerically compute the basic subroutines comprising these algorithms with near perfect accuracy.Moreover, it retains this level of accuracy while generalizing to unseen data and long sequences outside of the training distribution.

我们提出了神经执行引擎（NEEs），它利用学习到的掩码和受监督的执行痕迹来模仿子程序的功能，并表现出强大的泛化能力。

本文研究了一个用神经网络构建程序执行引擎的问题，提出了一个基于变压器的模型来学习基本的子程序，并将其应用于几个标准算法中。

本文讨论的是设计能够学习和实现一般程序的神经网络架构的问题。


Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks.However, the meta-learning literature thus far has focused on the task segmented setting, where at train-time, offline data is assumed to be split according to the underlying task, and at test-time, the algorithms are optimized to learn in a single task.In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with a time-varying task.We present meta-learning via online changepoint analysis (MOCA), an approach which augments a meta-learning algorithm with a differentiable Bayesian changepoint detection scheme.The framework allows both training and testing directly on time series data without segmenting it into discrete tasks.We demonstrate the utility of this approach on a nonlinear meta-regression benchmark as well as two meta-image-classification benchmarks.

贝叶斯变化点检测能够直接从时间序列数据中进行元学习。

本文考虑了在任务非分割设置中的元学习，并应用贝叶斯在线变化点检测与元学习。

本文将元学习推向任务不分割的环境，其中MOCA框架采用贝叶斯变化点估计方案进行任务变化检测。


People with high-frequency hearing loss rely on hearing aids that employ frequency lowering algorithms.These algorithms shift some of the sounds from the high frequency band to the lower frequency band where the sounds become more perceptible for the people with the condition.Fricative phonemes have an important part of their content concentrated in high frequency bands.It is important that the frequency lowering algorithm is activated exactly for the duration of a fricative phoneme, and kept off at all other times.Therefore, timely (with zero delay) and accurate fricative phoneme detection is a key problem for high quality hearing aids.In this paper we present a deep learning based fricative phoneme detection algorithm that has zero detection delay and achieves state-of-the-art fricative phoneme detection accuracy on the TIMIT Speech Corpus.All reported results are reproducible and come with easy to use code that could serve as a baseline for future research.

基于深度学习的零延迟摩擦音检测方法

本文采用有监督的深度学习方法来检测摩擦音音素的确切持续时间，以改进实际的降频算法。


Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction.To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed.We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time.When applied to online speech recognition, we obtain state-of-the-art results and match the performance of a model using an offline soft attention mechanism.In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model.

一种在线和线性时间的注意力机制，在输入序列中自适应定位的块上执行软性注意力。

本文对[1]中的单调注意力提出了一个小的修改，即在单调注意力所预测的片段上增加一个软性注意。

本文提出了对以前的单调注意模型的扩展（Raffel等人，2017），以关注一个固定大小的窗口，直到对齐位置。


We present a framework for automatically ordering image patches that enables in-depth analysis of dataset relationship to learnability of a classification task using convolutional neural network.An image patch is a group of pixels residing in a continuous area contained in the sample.Our preliminary experimental results show that an informed smart shuffling of patches at a sample level can expedite training by exposing important features at early stages of training.In addition, we conduct systematic experiments and provide evidence that CNNâ€™s generalization capabilities do not correlate with human recognizable features present in training samples.We utilized the framework not only to show that spatial locality of features within samples do not correlate with generalization, but also to expedite convergence while achieving similar generalization performance.Using multiple network architectures and datasets, we show that ordering image regions using mutual information measure between adjacent patches, enables CNNs to converge in a third of the total steps required to train the same network without patch ordering.

开发依靠补丁重排的新技术，以便能够详细分析数据集与训练和概括性能的关系。


Producing agents that can generalize to a wide range of environments is a significant challenge in reinforcement learning.One method for overcoming this issue is domain randomization, whereby at the start of each training episode some parameters of the environment are randomized so that the agent is exposed to many possible variations.However, domain randomization is highly inefficient and may lead to policies with high variance across domains.In this work, we formalize the domain randomization problem, and show that minimizing the policy's Lipschitz constant with respect to the randomization parameters leads to low variance in the learned policies.We propose a method where the agent only needs to be trained on one variation of the environment, and its learned state representations are regularized during training to minimize this constant.We conduct experiments that demonstrate that our technique leads to more efficient and robust learning than standard domain randomization, while achieving equal generalization scores.

我们使用一种新的正则化技术，产生了对广泛环境具有良好概括性的强化学习代理。

本文介绍了强化学习领域随机化中的高变异策略挑战，并主要关注视觉化随机化问题，不同的随机化领域仅在状态空间上存在差异，而基础奖励和动力学是相同的。

为了提高深度RL代理在不同视觉模式的任务中的泛化能力，本文提出了一种简单的正则化技术，用于领域随机化。


Claims from the fields of network neuroscience and connectomics suggest that topological models of the brain involving complex networks are of particular use and interest.The field of deep neural networks has mostly left inspiration from these claims out.In this paper, we propose three architectures and use each of them to explore the intersection of network neuroscience and deep learning in an attempt to bridge the gap between the two fields.Using the teachings from network neuroscience and connectomics, we show improvements over the ResNet architecture, we show a possible connection between early training and the spectral properties of the network, and we show the trainability of a DNN based on the neuronal network of C.Elegans.

我们探索网络神经科学和深度学习的交叉点。


Creating a knowledge base that is accurate, up-to-date and complete remains a significant challenge despite substantial efforts in automated knowledge base construction.  In this paper, we present Alexandria -- a system for unsupervised, high-precision knowledge base construction.Alexandria uses a probabilistic program to define a process of converting knowledge base facts into unstructured text.  Using probabilistic inference, we can invert this program and so retrieve facts, schemas and entities from web text.The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources.Because Alexandria does not require labelled training data, knowledge bases can be constructed with the minimum of manual input.We demonstrate this by constructing a high precision (typically 97\%+) knowledge base for people from a single seed fact.

本文提出了一个非监督的、高精度的知识库构建系统，使用概率程序来定义将知识库事实转换为非结构化文本的过程。

关于现有知识库的概述，该知识库是用概率模型构建的，与其他知识库建构方法YAGO2、NELL、Knowledge Vault和DeepDive进行了评估。

本文使用一个描述实体的事实可以在文本和大量网页中实现的概率程序，来学习使用单一的种子事实进行关于人的事实提取。


Recent advances have made it possible to create deep complex-valued neural networks.Despite this progress, many challenging learning tasks have yet to leverage the power of complex representations.Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain.As a case study, we perform audio source separation in the Fourier domain.Our new method takes advantage of the convolution theorem which states that the Fourier transform of two convolved signals is the elementwise product of their Fourier transforms.Our novel method is based on a complex-valued version of Feature-Wise Linear Modulation (FiLM) and serves as the keystone of our proposed signal extraction method.We also introduce a new and explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram.Using the Wall Street Journal Dataset, we compared our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.

傅里叶域中的新信号提取方法


We propose an implementation of GNN that predicts and imitates the motion be- haviors from observed swarm trajectory data.The networkâ€™s ability to capture interaction dynamics in swarms is demonstrated through transfer learning.We finally discuss the inherent availability and challenges in the scalability of GNN, and proposed a method to improve it with layer-wise tuning and mixing of data enabled by padding.

提高图形神经网络在模仿学习和预测蜂群运动上的可扩展性 本文提出了一个新的时间序列模型，用于学习一连串的图形。 这项工作考虑了多代理系统中的序列预测问题。


Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.Despite their effectiveness, the number of parameters in an embedding layer increases linearly with the number of symbols and poses a critical challenge on memory and storage constraints.In this work, we propose a generic and end-to-end learnable compression framework termed differentiable product quantization (DPQ).We present two instantiations of DPQ that leverage different approximation techniques to enable differentiability in end-to-end learning.Our method can readily serve as a drop-in alternative for any existing embedding layer.Empirically, DPQ offers significant compression ratios (14-238x) at negligible or no performance cost on 10 datasets across three different language tasks.

我们提出了一个可微分的产品量化框架，可以在没有性能成本的情况下减少端到端训练中的嵌入层的大小。

本文致力于研究压缩嵌入层以实现低内存推理的方法，其中压缩嵌入是以可微分的端到端方式与特定任务模型一起学习。


For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because they provide the conditional mean.Modal regression approaches aim to instead find the conditional mode, but are restricted to nonparametric approaches.Such approaches can be difficult to scale, and make it difficult to benefit from parametric function approximation, like neural networks, which can learn complex relationships between inputs and targets.In this work, we propose a parametric modal regression algorithm, by using the implicit function theorem to develop an objective for learning a joint parameterized function over inputs and targets.We empirically demonstrate on several synthetic problems that our method(i) can learn multi-valued functions and produce the conditional modes,(ii) scales well to high-dimensional inputs and(iii) is even more effective for certain unimodal problems, particularly for high frequency data where the joint function over inputs and targets can better capture the complex relationship between them.We conclude by showing that our method provides small improvements on two regression datasets that have asymmetric distributions over the targets.

我们介绍了一种简单而新颖的模态回归算法，该算法易于扩展到大型问题。 本文提出了一种隐含函数的方法来学习多模态回归的模式。 本工作提出了一种参数化的方法，利用多模分布的隐含函数定理估计条件模式。


Deep reinforcement learning algorithms require large amounts of experience to learn an individual task.While in principle meta-reinforcement learning (meta-RL) algorithms enable agents to learn new skills from small amounts of experience, several major challenges preclude their practicality.Current methods rely heavily on on-policy experience, limiting their sample efficiency.They also lack mechanisms to reason about task uncertainty when adapting to new tasks, limiting their effectiveness in sparse reward problems.In this paper, we address these challenges by developing an off-policy meta-RL algorithm that disentangles task inference and control.In our approach, we perform online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience.This probabilistic interpretation enables posterior sampling for structured and efficient exploration.We demonstrate how to integrate these task variables with off-policy RL algorithms to achieve both meta-training and adaptation efficiency.Our method outperforms prior algorithms in sample efficiency by 20-100X as well as in asymptotic performance on several meta-RL benchmarks.

将概率任务变量的变异推理与非政策性RL相结合，实现高效的样本meta-RL

本文提出在元训练时间内使用非政策性RL，极大地提高Meta-RL方法的采样效率。


Knowledge bases, massive collections of facts (RDF triples) on diverse topics, support vital modern applications.However, existing knowledge bases contain very little data compared to the wealth of information on the Web.This is because the industry standard in knowledge base creation and augmentation suffers from a serious bottleneck: they rely on domain experts to identify appropriate web sources to extract data from.Efforts to fully automate knowledge extraction have failed to improve this standard: these automated systems are able to retrieve much more data and from a broader range of sources, but they suffer from very low precision and recall.As a result, these large-scale extractions remain unexploited.In this paper, we present MIDAS, a system that harnesses the results of automated knowledge extraction pipelines to repair the bottleneck in industrial knowledge creation and augmentation processes.MIDAS automates the suggestion of good-quality web sources and describes what to extract with respect to augmenting an existing knowledge base.We make three major contributions.First, we introduce a novel concept, web source slices, to describe the contents of a web source.Second, we define a profit function to quantify the value of a web source slice with respect to augmenting an existing knowledge base.Third, we develop effective and highly-scalable algorithms to derive high-profit web source slices.We demonstrate that MIDAS produces high-profit results and outperforms the baselines significantly on both real-word and synthetic datasets.

本文的重点是为工业知识库扩增管道确定高质量的网络资源。