We explore the match prediction problem where one seeks to estimate the likelihood of a group of M items preferred over another, based on partial group comparison data.Challenges arise in practice.As existing state-of-the-art algorithms are tailored to certain statistical models, we have different best algorithms across distinct scenarios.Worse yet, we have no prior knowledge on the underlying model for a given scenario.These call for a unified approach that can be universally applied to a wide range of scenarios and achieve consistently high performances.To this end, we incorporate deep learning architectures so as to reflect the key structural features that most state-of-the-art algorithms, some of which are optimal in certain settings, share in common.This enables us to infer hidden models underlying a given dataset, which govern in-group interactions and statistical patterns of comparisons, and hence to devise the best algorithm tailored to the dataset at hand.Through extensive experiments on synthetic and real-world datasets, we evaluate our framework in comparison to state-of-the-art algorithms.It turns out that our framework consistently leads to the best performance across all datasets in terms of cross entropy loss and prediction accuracy, while the state-of-the-art algorithms suffer from inconsistent performances across different datasets.Furthermore, we show that it can be easily extended to attain satisfactory performances in rank aggregation tasks, suggesting that it can be adaptable for other tasks as well.

我们研究了在匹配预测问题上采用神经网络的优点，在这个问题上，人们试图根据部分组别比较数据来估计一组M项比另一组更受欢迎的可能性。

本文提出了一种深度神经网络对集合排名问题的解决方案，并为这项任务设计了一个架构，其灵感来自于以前的人工设计算法。

本文提供了一种使用深度学习架构来解决匹配预测问题的技术。


Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients.  Recent work on Unitary Recurrent Neural Networks (uRNNs) have been used to address this issue and in some cases, exceed the capabilities of Long Short-Term Memory networks (LSTMs).  We propose a simpler and novel update scheme to maintain orthogonal recurrent weight matrices without using complex valued matrices.This is done by parametrizing with a skew-symmetric matrix using the Cayley transform.Such a parametrization is unable to represent matrices with negative one eigenvalues, but this limitation is overcome by scaling the recurrent weight matrix by a diagonal matrix consisting of ones and negative ones.  The proposed training scheme involves a straightforward gradient calculation and update step.In several experiments, the proposed scaled Cayley orthogonal recurrent neural network (scoRNN) achieves superior results with fewer trainable parameters than other unitary RNNs.

在RNN中保持正交循环权重矩阵的一种新方法。

介绍了一种在神经网络中学习循环参数矩阵的方案，该方案使用Cayley变换和缩放权重矩阵。

本文提出了一种RNN的重新参数化，即用Cayley转换的偏斜对称矩阵来保持循环权重矩阵的正交性。

新的RNN参数化允许相对容易地表示正交权重矩阵。


A large number of natural language processing tasks exist to analyze syntax, semantics, and information content of human language.These seemingly very different tasks are usually solved by specially designed architectures.In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format consisting of labeling spans and relations between spans, thus a single task-independent model can be used across different tasks.We perform extensive experiments to test this insight on 10 disparate tasks as broad as dependency parsing (syntax), semantic role labeling (semantics), relation extraction (information content), aspect based sentiment analysis (sentiment), and many others, achieving comparable performance as state-of-the-art specialized models.We further demonstrate benefits in multi-task learning.We convert these datasets into a unified format to build a benchmark, which provides a holistic testbed for evaluating future models for generalized natural language analysis.

我们使用一个单一的模型来解决大量的自然语言分析任务，以统一的跨度关系格式来制定这些任务。

本文将广泛的自然语言处理任务概括为一个基于跨度的框架，并提出了一个解决所有这些问题的通用架构。

这项工作提出了各种短语和标记级NLP任务的统一表述。


Large matrix inversions have often been cited as a major impediment to scaling Gaussian process (GP) models.With the use of GPs as building blocks for ever more sophisticated Bayesian deep learning models, removing these impediments is a necessary step for achieving large scale results.We present a variational approximation for a wide range of GP models that does not require a matrix inverse to be performed at each optimisation step.Our bound instead directly parameterises a free matrix, which is an additional variational parameter.At the local maxima of the bound, this matrix is equal to the matrix inverse.We prove that our bound gives the same guarantees as earlier variational approximations.We demonstrate some beneficial properties of the bound experimentally, although significant wall clock time speed improvements will require future improvements in optimisation and implementation.

我们提出了一个GP模型的变异下限，可以在不计算昂贵的矩阵运算（如求逆）的情况下进行优化，同时提供与现有变异近似值相同的保证。


It has been shown that using geometric spaces with non-zero curvature instead of plain Euclidean spaces with zero curvature improves performance on a range of Machine Learning tasks for learning representations.Recent work has leveraged these geometries to learn latent variable models like Variational Autoencoders (VAEs) in spherical and hyperbolic spaces with constant curvature.While these approaches work well on particular kinds of data that they were designed for e.g.~tree-like data for a hyperbolic VAE, there exists no generic approach unifying all three models.We develop a Mixed-curvature Variational Autoencoder, an efficient way to train a VAE whose latent space is a product of constant curvature Riemannian manifolds, where the per-component curvature can be learned.This generalizes the Euclidean VAE to curved latent spaces, as the model essentially reduces to the Euclidean VAE if curvatures of all latent space components go to 0.

以恒定曲率黎曼流形的产物为模型的潜空间的变异自动编码器在图像重建方面比单一流形的变异自动编码器有所改善。

本文介绍了VAE概念的一般表述，其潜在空间由弯曲流形组成。

本文是关于在非欧几里得空间发展VAE。


Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery.We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties.Remarkably, we observe that when generated molecules are iteratively fed back into the translator, molecular compound attributes improve with each step.We show that this finding is invariant to the choice of translation model, making this a "black box" algorithm.We call this method Black Box Recursive Translation (BBRT), a new inference method for molecular property optimization.This simple, powerful technique operates strictly on the inputs and outputs of any translation model.We obtain new state-of-the-art results for molecular property optimization tasks using our simple drop-in replacement with well-known sequence and graph-based models.Our method provides a significant boost in performance relative to its non-recursive peers with just a simple "``for" loop.Further, BBRT is highly interpretable, allowing users to map the evolution of newly discovered compounds from known starting points.

我们介绍了一种使用翻译框架对化合物进行反复优化的黑箱算法。

作者将分子优化框定为一个序列到序列的问题，并扩展了现有的改进分子的方法，表明它有利于优化logP，但不利于优化QED。

本文在为分子优化开发的现有翻译模型的基础上，对序列到序列或图到图的翻译模型进行了迭代使用。


Deep Neural Networks (DNNs) are increasingly deployed in cloud servers and autonomous agents due to their superior performance.The deployed DNN is either leveraged in a white-box setting (model internals are publicly known) or a black-box setting (only model outputs are known) depending on the application.A practical concern in the rush to adopt DNNs is protecting the models against Intellectual Property (IP) infringement.We propose BlackMarks, the first end-to-end multi-bit watermarking framework that is applicable in the black-box scenario.BlackMarks takes the pre-trained unmarked model and the ownerâ€™s binary signature as inputs.The output is the corresponding marked model with specific keys that can be later used to trigger the embedded watermark.To do so, BlackMarks first designs a model-dependent encoding scheme that maps all possible classes in the task to bit â€˜0â€™ and bit â€˜1â€™.Given the ownerâ€™s watermark signature (a binary string), a set of key image and label pairs is designed using targeted adversarial attacks.The watermark (WM) is then encoded in the distribution of output activations of the DNN by fine-tuning the model with a WM-specific regularized loss.To extract the WM, BlackMarks queries the model with the WM key images and decodes the ownerâ€™s signature from the corresponding predictions using the designed encoding scheme.We perform a comprehensive evaluation of BlackMarksâ€™ performance on MNIST, CIFAR-10, ImageNet datasets and corroborate its effectiveness and robustness.BlackMarks preserves the functionality of the original DNN and incurs negligible WM embedding overhead as low as 2.054%.

提出了第一个利用DNN的输出进行多位元签名嵌入和提取的水印框架。

提出了一种在黑箱环境下对神经网络进行多位元水印的方法，并证明现有模型的预测可以携带一个多位元字符串，以后可以用来验证所有权。

本文提出了一种模型水印的方法，其中水印是作为微调程序的一部分嵌入到模型中的一个位元字符串。


Adversarial training provides a principled approach for training robust neural networks.From an optimization perspective, the adversarial training is essentially solving a minmax robust optimization problem.The outer minimization is trying to learn a robust classifier, while the inner maximization is trying to generate adversarial samples.Unfortunately, such a minmax problem is very difficult to solve due to the lack of convex-concave structure.This work proposes a new adversarial training method based on a general learning-to-learn framework.Specifically, instead of applying the existing hand-design algorithms for the inner problem, we learn an optimizer, which is parametrized as a convolutional neural network.At the same time, a robust classifier is learned to defense the adversarial attack generated by the learned optimizer.From the perspective of generative learning, our proposed method can be viewed as learning a deep generative model for generating adversarial samples, which is adaptive to the robust classification.Our experiments demonstrate that our proposed method significantly outperforms existing adversarial training methods on CIFAR-10 and CIFAR-100 datasets.

不知道如何优化？那就学着去优化吧! 本文提出了一种训练图像分类模型以抵抗L-无穷大扰动攻击的方法。 本文提出使用学习到学习的框架来学习一个攻击者。


In this work we introduce a new framework for performing temporal predictionsin the presence of uncertainty.It is based on a simple idea of disentangling com-ponents of the future state which are predictable from those which are inherentlyunpredictable, and encoding the unpredictable components into a low-dimensionallatent variable which is fed into the forward model.Our method uses a simple su-pervised training objective which is fast and easy to train.We evaluate it in thecontext of video prediction on multiple datasets and show that it is able to consi-tently generate diverse predictions without the need for alternating minimizationover a latent space or adversarial training.

一种简单易行的时间序列多模态预测的训练方法。

本文介绍了一个时间序列预测模型，该模型学习了一个确定性的映射，并训练另一个网络来预测未来的帧，给定的输入和第一个网络的剩余误差。

本文提出了一个不确定条件下的预测模型，他们把确定性的组件预测和不确定性的组件预测分开。


Conducting reinforcement-learning experiments can be a complex and timely process.A full experimental pipeline will typically consist of a simulation of an environment, an implementation of one or many learning algorithms, a variety of additional components designed to facilitate the agent-environment interplay, and any requisite analysis, plotting, and logging thereof.In light of this complexity, this paper introduces simple rl, a new open source library for carrying out reinforcement learning experiments in Python 2 and 3 with a focus on simplicity.The goal of simple_rl is to support seamless, reproducible methods for running reinforcement learning experiments.This paper gives an overview  of the core design philosophy of the package, how it differs from existing libraries, and showcases its central features.

本文介绍了simple_rl，这是一个新的开源库，用于在Python 2和3中进行强化学习实验，重点是简单性。


Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution.Recent studies have proposed stabilizing the training process for the WGAN and implementing the Lipschitz constraint.In this study, we prove the local stability of optimizing the simple gradient penalty $\mu$-WGAN(SGP $\mu$-WGAN) under suitable assumptions regarding the equilibrium and penalty measure $\mu$.The measure valued differentiation concept is employed to deal with the derivative of the penalty terms, which is helpful for handling abstract singular measures with lower dimensional support.Based on this analysis, we claim that penalizing the data manifold or sample manifold is the key to regularizing the original WGAN with a gradient penalty.Experimental results obtained with unintuitive penalty measures that satisfy our assumptions are also provided to support our theoretical results.

本文通过引入量值微分的概念来处理简单梯度惩罚$mu$-WGAN优化的稳定性。
研究了具有平方的零中心梯度惩罚项的WGAN对一个一般措施的影响。
描述了梯度惩罚的Wasserstein GAN的收敛性。


We present Random Partition Relaxation (RPR), a method for strong quantization of the parameters of convolutional neural networks to binary (+1/-1) and ternary (+1/0/-1) values.Starting from a pretrained model, we first quantize the weights and then relax random partitions of them to their continuous values for retraining before quantizing them again and switching to another weight partition for further adaptation.  We empirically evaluate the performance of RPR with ResNet-18, ResNet-50 and GoogLeNet on the ImageNet classification task for binary and ternary weight networks.We show accuracies beyond the state-of-the-art for binary- and ternary-weight GoogLeNet and competitive performance for ResNet-18 and ResNet-50 using a SGD-based training method that can easily be integrated into existing frameworks.

基于随机松弛权重分区的交替优化的二元和三元权重网络的最先进训练方法

本文提出了一种优化三元神经网络的新训练方案。

作者提出了RPR，一种随机分配和量化权重并训练剩余参数的方法，然后在交替循环中进行放松，以训练量化的模型。


Learning long-term dependencies is a key long-standing challenge of recurrent neural networks (RNNs).Hierarchical recurrent neural networks (HRNNs) have been considered a promising approach as long-term dependencies are resolved through shortcuts up and down the hierarchy.Yet, the memory requirements of Truncated Backpropagation Through Time (TBPTT) still prevent training them on very long sequences.In this paper, we empirically show that in (deep) HRNNs, propagating gradients back from higher to lower levels can be replaced by locally computable losses, without harming the learning capability of the network, over a wide range of tasks.This decoupling by local losses reduces the memory requirements of training by a factor exponential in the depth of the hierarchy in comparison to standard TBPTT.

我们用一个辅助损失来代替分层RNN中的一些梯度路径。我们表明，这可以减少内存成本，同时保持性能。

本文介绍了一种分层的RNN架构，可以训练更有效地内存。

本文建议使用辅助损失来解开RNN中不同层次的结构。


In a typical deep learning approach to a computer vision task, Convolutional Neural Networks (CNNs) are used to extract features at varying levels of abstraction from an image and compress a high dimensional input into a lower dimensional decision space through a series of transformations.In this paper, we investigate how a class of input images is eventually compressed over the course of these transformations.In particular, we use singular value decomposition to analyze the relevant variations in feature space.These variations are formalized as the effective dimension of the embedding.We consider how the effective dimension varies across layers within class.We show that across datasets and architectures, the effective dimension of a class increases before decreasing further into the network, suggesting some sort of initial whitening transformation.Further, the decrease rate of the effective dimension deeper in the network corresponds with training performance of the model.

做好分类工作的神经网络在将点压缩到较少的维度之前，会将其投影成更多的球形。


Deep learning methods have achieved high performance in sound recognition tasks.Deciding how to feed the training data is important for further performance improvement.We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning).Our strategy is to learn a discriminative feature space by recognizing the between-class sounds as between-class sounds.We generate between-class sounds by mixing two sounds belonging to different classes with a random ratio.We then input the mixed sound to the model and train the model to output the mixing ratio.The advantages of BC learning are not limited only to the increase in variation of the training data; BC learning leads to an enlargement of Fisherâ€™s criterion in the feature space and a regularization of the positional relationship among the feature distributions of the classes.The experimental results show that BC learning improves the performance on various sound recognition networks, datasets, and data augmentation schemes, in which BC learning proves to be always beneficial.Furthermore, we construct a new deep sound recognition network (EnvNet-v2) and train it with BC learning.As a result, we achieved a performance surpasses the human level.

我们提出了一种新的深度声音识别的学习方法，名为BC学习。

作者定义了一个新的学习任务，要求DNN预测两个不同类别的声音之间的混合比例，以提高最终学习网络的辨别能力。

提出了一种通过生成 "类间 "训练样本来提高通用学习方法性能的方法，并介绍了所提出技术的基本直觉和必要性。


Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on.While numerous studies have been conducted, most existing works assume that the data from different sources or across different locations are equally reliable.Due to cost, accessibility, or other factors, it is inevitable that the data quality could vary, which introduces significant biases into the model and leads to unreliable prediction results.The problem could be exacerbated in black-box prediction models, such as deep neural networks.In this paper, we propose a novel solution that can automatically infer data quality levels of different sources through local variations of spatiotemporal signals without explicit labels.Furthermore, we integrate the estimate of data quality level with graph convolutional networks to exploit their efficient structures.We evaluate our proposed method on forecasting temperatures in Los Angeles.

我们提出了一种方法，为时空预测推断出随时间变化的数据质量水平，而无需明确指定标签。

引入了一个新的数据质量定义，它依赖于（Zhou和Scholkopf）中定义的区域变化的概念，并将其扩展到多个异质数据源。

这项工作提出了一种用时变图模型评估不同数据源质量的新方法，质量水平作为目标函数中的正则化项。


Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts.In contrast, recent advances in 3D shape sensing focus more on low-level geometry but less on these higher-level relationships.In this paper, we propose 3D shape programs, integrating bottom-up recognition systems with top-down, symbolic program structure to capture both low-level geometry and high-level structural priors for 3D shapes.Because there are no annotations of shape programs for real shapes, we develop neural modules that not only learn to infer 3D shape programs from raw, unannotated shapes, but also to execute these programs for shape reconstruction.After initial bootstrapping, our end-to-end differentiable model learns 3D shape programs by reconstructing shapes in a self-supervised manner.Experiments demonstrate that our model accurately infers and executes 3D shape programs for highly complex shapes from various categories.It can also be integrated with an image-to-shape module to infer 3D shape programs directly from an RGB image, leading to 3D shape reconstructions that are both more accurate and more physically plausible.

我们提出了3D形状程序，这是一种结构化的、组合式的形状表示。我们的模型可以学习推论和执行形状程序来解释三维形状。

一种推断给定3D模型的形状程序的方法，其结构包括一个对3D形状进行编码并输出指令的循环网络，以及一个将程序变成3D的第二模块。

本文介绍了一个由ShapeProgram给出的3D形状的高阶语义描述。


Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention  thanks to its encouraging performance on a variety of control tasks.Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment.In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks.Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvement on the task performance, and the improvement is typically more significant when the task is more difficult.We also compare with the widely used entropy regularization and find $L_2$ regularization is generally better.Our findings are further confirmed to be robust against the choice of training hyperparameters.We also study the effects of regularizing different components and find that only regularizing the policy network is typically enough.We hope our study provides guidance for future practices in regularizing policy optimization algorithms.

我们表明，传统的正则化方法（如$L_2$，随机失活），在RL方法中基本上被忽略了，但在策略优化中可以非常有效。

作者研究了强化学习领域现有的一套直接策略优化方法，并详细调查了法规对遵循这些方法的代理人的性能和行为的影响。

本文提供了一个关于正则化对训练环境中多个连续控制任务的策略优化方法的性能影响的研究。


We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images.The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts.We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection.To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure.To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives.In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements.We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as strong baseline.Preliminary results indicate that the task poses a significant machine learning challenge.We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data.

我们提出了一个回答问题的数据集FigureQA，作为开发能够从数据的视觉表现中直观地识别模式的模型的第一步。

本文介绍了一个关于数字的模板化问题回答的数据集，涉及对数字元素的推理。

本文介绍了一个名为Figure-QA的新的视觉推理数据集，该数据集由14万张图象和1.55万个QA对组成，可以帮助开发能够从数据的视觉表示中提取有用信息的模型。


In this paper, I discuss some varieties of explanation that can arisein intelligent agents.I distinguish between process accounts, whichaddress the detailed decisions made during heuristic search, andpreference accounts, which clarify the ordering of alternativesindependent of how they were generated.I also hypothesize which types of users will appreciate which types of explanation.In addition, I discuss three facets of multi-step decision making-- conceptual inference, plan generation, and plan execution --in which explanations can arise.I also consider alternative waysto present questions to agents and for them provide their answers.

这份立场文件分析了规划和相关系统中可能出现的不同类型的自我解释。 讨论了解释的不同方面，特别是在顺序决策的背景下。


Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details.Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery.To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion:(i) co-registration,(ii) fusion,(iii) up-sampling, and(iv) registration-at-the-loss.Co-registration of low-res views is learned implicitly through a reference-frame channel, with no explicit registration mechanism.We learn a global fusion operator that is applied recursively on an arbitrary number of low-res pairs.We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet.We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth observation data at scale.Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery.

第一个深度学习的MFSR方法，以端到端的方式解决注册、融合、上采样的问题。

本文提出了一种端到端的多帧超级解析度算法，该算法依赖于一个成对的共同注册和融合块（卷积剩余块），嵌入到一个估计超级解析度图像的编码器-解码器网络 "HighRes-net"。

本文提出了一个包括递归融合到共同注册损失的框架，以解决超解析度结果和高分辨率标签没有像素对齐的问题。


Large mini-batch parallel SGD is commonly used for distributed training of deep networks.Approaches that use tightly-coupled exact distributed averaging based on AllReduce are sensitive to slow nodes and high-latency communication.In this work we show the applicability of Stochastic Gradient Push (SGP) for distributed training.SGP uses a gossip algorithm called PushSum for approximate distributed averaging, allowing for much more loosely coupled communications which can be beneficial in high-latency or high-variability scenarios.The tradeoff is that approximate distributed averaging injects additional noise in the gradient which can affect the train and test accuracies.We prove that SGP converges to a stationary point of smooth, non-convex objective functions.Furthermore, we validate empirically the potential of SGP.For example, using 32 nodes with 8 GPUs per node to train ResNet-50 on ImageNet, where nodes communicate over 10Gbps Ethernet, SGP completes 90 epochs in around 1.5 hours while AllReduce SGD takes over 5 hours, and the top-1 validation accuracy of SGP remains within 1.2% of that obtained using AllReduce SGD.

对于高延迟网络上的分布式训练，使用基于流言的近似分布式平均法，而不是像AllReduce那样的精确分布式平均法。

作者提出使用流言算法作为计算一组工人的近似平均数的一般方法

本文证明了SGP对于非凸平滑函数的收敛性，并表明SGP在低延迟环境下可以实现显着的速度提升，而不会牺牲太多的预测性能。


In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on.The proposed system, phredGAN has a persona-based HRED generator (PHRED) and a conditional discriminator.We also explore two approaches to accomplish the conditional discriminator: (1) $phredGAN_a$, a system that passes the attribute representation as an additional input into a traditional adversarial discriminator, and (2) $phredGAN_d$, a dual discriminator system which in addition to the adversarial discriminator, collaboratively predicts the attribute(s) that generated the input utterance.To demonstrate the superior performance of phredGAN over the persona SeqSeq model, we experiment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC) and TV series transcripts from the Big Bang Theory and Friends.Performance comparison is made with respect to a variety of quantitative measures as well as crowd-sourced human evaluation.We also explore the trade-offs from using either variant of $phredGAN$ on datasets with many but weak attribute modalities (such as with Big Bang Theory and Friends) and ones with few but strong attribute modalities (customer-agent interactions in Ubuntu dataset).

本文开发了一个对抗性学习框架，用于具有角色的神经对话模型。

本文提出了对hredGAN的扩展，以同时学习一组代表每个说话人的角色的属性嵌入，并生成基于角色的反应。


We introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions.To simulate properties of biological systems we add the costs penalizing long connections and the proximity of neurons in a two-dimensional space.Our experiments show that in the case where the network performs two different tasks, the neurons naturally split into clusters, where each cluster is responsible for processing a different task.This behavior not only corresponds to the biological systems, but also allows for further insight into interpretability or continual learning.

生物启发的人工神经网络，由定位在二维空间的神经元组成，能够形成独立的群体，执行不同的任务。


The transformer has become a central model for many NLP tasks from translation to language modeling to representation learning.Its success demonstrates the effectiveness of stacked attention as a replacement for recurrence for many tasks.In theory attention also offers more insights into the modelâ€™s internal decisions; however, in practice when stacked it quickly becomes nearly as fully-connected as recurrent models.In this work, we propose an alternative transformer architecture, discrete transformer, with the goal of better separating out internal model decisions.The model uses hard attention to ensure that each step only depends on a fixed context.Additionally, the model uses a separate â€œsyntacticâ€ controller to separate out network structure from decision making.Finally we show that this approach can be further sparsified with direct regularization.Empirically, this approach is able to maintain the same level of performance on several datasets, while discretizing reasoning decisions over the data.

离散转化器，它使用硬注意力来确保每一步只取决于一个固定的环境。

本文介绍了对标准转化器架构的修改，目的是提高可解释性，同时保留NLP任务的性能。

本文提出了三个离散变换器：一个基于离散和随机的Gumbel-Softmax注意力模块，一个双流句法和语义变换器，以及稀疏性正则化。


Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states.We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex.We use representational similarity analysis (RSA) to compare PredNet representations to functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from the Algonauts Project (Cichy et al., 2019).In contrast to previous findings in the literature (Khaligh-Razavi & Kriegeskorte, 2014), we report empirical data suggesting that unsupervised models trained to predict frames of videos without further fine-tuning may outperform supervised image classification baselines in terms of correlation to spatial (fMRI) and temporal (MEG) data.

我们展示了经验证据，预测性编码模型产生的表征比监督性图像识别模型与大脑数据更相关。


The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples.Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest.Ideally one would like to allow both the data for the current task and for previous related tasks to self-organize the learning system in such a way that commonalities and differences between the tasks are learned in a data-driven fashion.We develop a framework for learning multiple tasks simultaneously, based on sharing features that are common to all tasks, achieved through the use of a modular deep feedforward neural network consisting of shared branches, dealing with the common features of all tasks, and private branches, learning the specific unique aspects of each task.Once an appropriate weight sharing architecture has been established, learning takes place through standard algorithms for feedforward networks, e.g., stochastic gradient descent and its variations.The method deals with meta-learning (such as domain adaptation, transfer and multi-task learning) in a unified fashion, and can easily deal with data arising from different types of sources.Numerical experiments demonstrate the effectiveness of learning in domain adaptation and transfer learning setups, and provide evidence for the flexible and task-oriented representations arising in the network.

使用具有特定任务和共享权重的自动编码器对处理转移和多任务学习的通用框架。

提出了一个用深度神经网络进行端到端转移学习/领域适应的通用框架。

本文提出了一个允许深度神经网络架构在不同数据集上共享参数的模型，并将其应用于转移学习。

本文着重于从多个领域的数据中学习共同特征，最后提出了一个用于多任务、半监督和转移学习的通用架构。


Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.We unite the two via adaptive neural trees (ANTs), a model that incorporates representation learning into edges, routing functions and leaf nodes of a decision tree, along with a backpropagation-based training algorithm that adaptively grows the architecture from primitive modules (e.g., convolutional layers).ANTs allow increased interpretability via hierarchical clustering, e.g., learning meaningful class associations, such as separating natural vs. man-made objects.We demonstrate this on classification and regression tasks, achieving over 99% and 90% accuracy on the MNIST and CIFAR-10 datasets, and outperforming standard neural networks, random forests and gradient boosted trees on the SARCOS dataset.Furthermore, ANT optimisation naturally adapts the architecture to the size and complexity of the training data.

我们提出了一个结合决策树和神经网络的框架，并在图像分类任务中表明它享有两种方法的互补优势，同时解决了先前工作的局限性。

作者通过将神经网络的表征学习和梯度优化与决策树的架构学习相结合，提出了一个新的模型--自适应神经树。

本文提出了自适应神经树的方法，将深度神经网和决策树这两种学习规范结合起来。


While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language.XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark.With improvements of up to 4.8, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu.XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language.On the SQuAD question answering task, we see that XLDA provides a 1.0 performance increase on the English evaluation set.Comprehensive experiments suggest that most languages are effective as cross-lingual augmentors, that XLDA is robust to a wide range of translation quality, and that XLDA is even more effective for randomly initialized models than for pretrained models.

在训练期间翻译部分输入内容可以提高跨语言的性能。

本文提出了一种跨语言的数据增强方法，以改善语言推理和问题回答任务。

本文提出用启发式交换来增强跨语言数据，使用联合的翻译，就像双语人类在代码转换中做的那样。


Training conditional generative latent-variable models is challenging in scenarios where the conditioning signal is very strong and the decoder is expressive enough to generate a plausible output given only the condition; the generative model tends to ignore the latent variable, suffering from posterior collapse. We find, and empirically show, that one of the major reasons behind posterior collapse is rooted in the way that generative models are conditioned, i.e., through concatenation of the latent variable and the condition. To mitigate this problem, we propose to explicitly make the latent variables depend on the condition by unifying the conditioning and latent variable sampling, thus coupling them so as to prevent the model from discarding the root of variations. To achieve this, we develop a conditional Variational Autoencoder architecture that learns a distribution not only of the latent variables, but also of the condition, the latter acting as prior on the former. Our experiments on the challenging tasks of conditional human motion prediction and image captioning demonstrate the effectiveness of our approach at avoiding posterior collapse. Video results of our approach are anonymously provided in http://bit.ly/iclr2020

我们提出了一个条件变异自动编码器框架，在条件信号强大到足以让表达式解码器从中产生可信的输出的情况下，可以缓解后验坍塌。

本文考虑了强条件生成模型，并提出了一个目标函数和变异分布的参数化，从而使潜变量明确地取决于输入条件。

本文认为，当解码器以潜在变量和辅助信息的串联为条件时，那么后验坍塌的可能性比普通的VAE更大。


We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.  We propose a methodology for testing for statistical differences in model performances under several replications.To study this specific design, we attempt to reproduce results from three prominent papers: Matching Nets, Prototypical Networks, and TADAM.We analyze on the miniImagenet dataset on the standard classification task in the 5-ways, 5-shots learning setting at test time.We find that the selected implementations exhibit stability across random seed, and repeats.

我们提出了一项研究，即在控制随机种子的同时，受制于超参数和优化方案的变化，研究几种少样本学习算法的稳定性。

本文研究的是少样本学习的可重复性。


We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach.Accordingly, the choice of representation -- the mapping of observation space to goal space -- is crucial.To study this problem, we develop a notion of sub-optimality of a representation, defined in terms of expected reward of the optimal hierarchical policy using this representation.We derive expressions which bound the sub-optimality and show how these expressions can be translated to representation learning objectives which may be optimized in practice.Results on a number of difficult continuous-control tasks show that our approach to representation learning yields qualitatively better representations as well as quantitatively better hierarchical policies, compared to existing methods.

我们在分层强化学习的背景下，将对表征的次优性的约束转化为实际的训练目标。

作者提出了一种学习HRL表征的新方法，并指出了表征学习和约束次优性之间的有趣联系，从而形成了一种基于梯度的算法。

本文提出了一种处理学习表征背景下的次优性的方法，它指的是分级政体相对于任务奖励的次优性。


Heuristic search research often deals with finding algorithms for offline planning which aim to minimize the number of expanded nodes or planning time.In online planning, algorithms for real-time search or deadline-aware search have been considered before.However, in this paper, we are interested in the problem of {\em situated temporal planning} in which an agent's plan can depend on exogenous events in the external world, and thus it becomes important to take the passage of time into account during the planning process.  Previous work on situated temporal planning has proposed simple pruning strategies, as well as complex schemes for a simplified version of the associated metareasoning problem. In this paper, we propose a simple metareasoning technique,  called the crude greedy scheme, which can be applied in a situated temporal planner.Our empirical evaluation shows that the crude greedy scheme outperforms standard heuristic search based on cost-to-go estimates.

坐标式时空规划器中的元推理

本文讨论了位置时间规划的问题，提出了对Shperberg之前提出的贪婪策略的进一步简化。


Neural networks are vulnerable to small adversarial perturbations.Existing literature largely focused on understanding and mitigating the vulnerability of learned models.In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution.Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution.Our discovery of such sensitivity on data distribution is based on a study which disentangles the behaviors of clean accuracy and robust accuracy of the Bayes classifier.Empirical investigations further confirm our finding.We construct semantically-identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve comparable clean accuracies on them, but adversarially trained models achieve significantly different robustness accuracies.This counter-intuitive phenomenon indicates that input data distribution alone can affect the adversarial robustness of trained neural networks, not necessarily the tasks themselves.Lastly, we discuss the practical implications on evaluating adversarial robustness, and make initial attempts to understand this complex phenomenon.

PGD训练的模型的稳定性表现对图像数据集的语义保存转换很敏感，这意味着在实践中对稳定学习算法的评估很棘手。

论文澄清了干净和稳定的准确性之间的区别，并表明在保留其语义P(y|x)的同时改变输入数据的边际分布会影响模型的稳定性。

本文研究了在l-inf有界扰动下，分类器对对抗性输入的扰动缺乏稳定性的根源。


Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity.Typically this comparison is done either at the word level or at the sentence level, with no attempt to leverage the inherent structure of the sentence.When sentence structure is used for comparison, it is obtained during a non-differentiable pre-processing step, leading to propagation of errors.We introduce a model of structured alignments between sentences, showing how to compare two sentences by matching their latent structures.Using a structured attention mechanism, our model matches possible spans in the first sentence to possible spans in the second sentence, simultaneously discovering the tree structure of each sentence and performing a comparison, in a model that is fully differentiable and is trained only on the comparison objective.We evaluate this model on two sentence comparison tasks: the Stanford natural language inference dataset and the TREC-QA dataset.We find that comparing spans results in superior performance to comparing words individually, and that the learned trees are consistent with actual linguistic structures.

通过学习潜在的成分树结构，用嵌入为神经网络层的内-外算法的变体来匹配句子。

本文介绍了一种结构化的关注机制，以计算两个给定句子中所有可能的跨度之间的对齐分数。

本文提出了一个句子之间的结构化对齐模型，作为通过匹配句子的潜在结构进行比较的手段。


Learning disentangled representation from any unlabelled data is a non-trivial problem.In this paper we propose Information Maximising Autoencoder (InfoAE) where the encoder learns powerful disentangled representation through maximizing the mutual information between the representation and given information in an unsupervised fashion.We have evaluated our model on MNIST dataset and achieved approximately 98.9 % test accuracy while using complete unsupervised training.

以无监督的方式学习disentangle表征。

作者提出了一个框架，在这个框架中，自动编码器（E，D）被正则化，使其潜在表示与生成的潜在空间表示共享相互信息。


Effective training of neural networks requires much data.In the low-data regime,parameters are underdetermined, and learnt networks generalise poorly.DataAugmentation (Krizhevsky et al., 2012) alleviates this by using existing datamore effectively.However standard data augmentation produces only limitedplausible alternative data.Given there is potential to generate a much broader setof augmentations, we design and train a generative model to do data augmentation.The model, based on image conditional Generative Adversarial Networks, takesdata from a source domain and learns to take any data item and generalise itto generate other within-class data items.As this generative process does notdepend on the classes themselves, it can be applied to novel unseen classes of data.We show that a Data Augmentation Generative Adversarial Network (DAGAN)augments standard vanilla classifiers well.We also show a DAGAN can enhancefew-shot learning systems such as Matching Networks.We demonstrate theseapproaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, andVGG-Face data.In our experiments we can see over 13% increase in accuracy inthe low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9%to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot weobserve an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% inEMNIST (from 59.5% to 61.3%).

有条件的GANs可以生成其条件输入的数据增强样本，用于增强普通分类和单样本学习系统，如匹配网络和像素距离。

作者提出了一种进行数据增加的方法，即使用条件GAN将跨类转换映射到低维潜在空间中。


Answering questions about data can require understanding what parts of an input X influence the response Y. Finding such an understanding can be built by testing relationships between variables through a machine learning model.For example, conditional randomization tests help determine whether a variable relates to the response given the rest of the variables.However, randomization tests require users to specify test statistics.We formalize a class of proper test statistics that are guaranteed to select a feature when it provides information about the response even when the rest of the features are known.We show that f-divergences provide a broad class of proper test statistics.In the class of f-divergences, the KL-divergence yields an easy-to-compute proper test statistic that relates to the AMI.Questions of feature importance can be asked at the level of an individual sample.  We show that estimators from the same AMI test can also be used to find important features in a particular instance.We provide an example to show that perfect predictive models are insufficient for instance-wise feature selection.We evaluate our method on several simulation experiments, on a genomic dataset, a clinical dataset for hospital readmission, and on a subset of classes in ImageNet.Our method outperforms several baselines in various simulated datasets, is able to identify biologically significant genes, can select the most important predictors of a hospital readmission event, and is able to identify distinguishing features in an image-classification task.

我们开发了一个简单的基于回归的模型诊断特征选择方法，以解释具有FDR控制的数据生成过程，并在几个模拟、医疗和图像数据集上的表现超过了几个流行的基线。

本文提出了条件随机化检验的实际改进和新的检验统计量，证明了f-发散性是一种可能的选择，并表明KL-发散性取消了一些条件分布。

本文讨论了在输入中寻找有用的特征的问题，这些特征即使在以所有其他输入变量为条件时也取决于反应变量。

一种与模型无关的方法，提供关于输入特征对机器级模型反应的影响的解释，并为与模型无关的特征选择提供适当的测试统计数据。


Supervised learning depends on annotated examples, which are taken to be the ground truth.But these labels often come from noisy crowdsourcing platforms, like Amazon Mechanical Turk.Practitioners typically collect multiple labels per example and aggregate the results to mitigate noise (the classic crowdsourcing problem).Given a fixed annotation budget and unlimited unlabeled data, redundant annotation comes at the expense of fewer labeled examples.This raises two fundamental questions: (1) How can we best learn from noisy workers?(2) How should we allocate our labeling budget to maximize the performance of a classifier?We propose a new algorithm for jointly modeling labels and worker quality from noisy crowd-sourced data.The alternating minimization proceeds in rounds, estimating worker quality from disagreement with the current model and then updating the model by optimizing a loss function that accounts for the current estimate of worker quality.Unlike previous approaches, even with only one annotation per example, our algorithm can estimate worker quality.We establish a generalization error bound for models learned with our algorithm and establish theoretically that it's better to label many examples once (vs less multiply) when worker quality exceeds a threshold.Experiments conducted on both ImageNet (with simulated noisy workers) and MS-COCO (using the real crowdsourced labels) confirm our algorithm's benefits.

一种从嘈杂的众包注释中学习模型的新方法。

本文提出了一种从噪声标签中学习的方法，侧重于数据没有冗余标签的情况，并进行了理论和实验验证

本文重点关注从众学习问题，联合更新分类器权重和工人的混淆矩阵可以帮助解决稀有众包标签的估计问题。

提出了一种监督学习算法，用于对标签和工人的质量进行建模，并利用该算法研究在众包中需要多少多余，以及低多余度和大量的噪音实例是否会带来更好的标签。


Neural networks make mistakes.The reason why a mistake is made often remains a mystery.As such neural networks often are considered a black box.It would be useful to have a method that can give an explanation that is intuitive to a user as to why an image is misclassified.In this paper we develop a method for explaining the mistakes of a classifier model by visually showing what must be added to an image such that it is correctly classified.Our work combines the fields of adversarial examples, generative modeling and a correction technique based on difference target propagation to create an technique that creates explanations of why an image is misclassified.In this paper we explain our method and demonstrate it on MNIST and CelebA.This approach could aid in demystifying neural networks for a user.

解释神经网络为何对图像分类有误的新方法

本文提出了一种解释神经网络的分类错误的方法。

旨在更好地理解神经网络的分类，探索变异自动编码器的潜空间，并考虑潜空间的扰动，以获得正确的分类。


In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand.Such ramified networks typically start with a number of shared layers, after which different tasks branch out into their own sequence of layers.Understandably, as the number of possible network configurations is combinatorially large, deciding what layers to share and where to branch out becomes cumbersome.Prior works have either relied on ad hoc methods to determine the level of layer sharing, which is suboptimal, or utilized neural architecture search techniques to establish the network design, which is considerably expensive.In this paper, we go beyond these limitations and propose a principled approach to automatically construct branched multi-task networks, by leveraging the employed tasks' affinities.Given a specific budget, i.e. number of learnable parameters, the proposed approach generates architectures, in which shallow layers are task-agnostic, whereas deeper ones gradually grow more task-specific.Extensive experimental analysis across numerous, diverse multi-tasking datasets shows that, for a given budget, our method consistently yields networks with the highest performance, while for a certain performance threshold it requires the least amount of learnable parameters.

一种自动构建分叉多任务网络的方法，在不同的多任务数据集上进行了强有力的实验评估。

本文提出了一种基于树状结构的新型软参数共享多任务学习框架。

本文提出了一种推断多任务网络结构的方法，以确定网络的哪一部分应该在不同任务之间共享。


Typical recent neural network designs are primarily convolutional layers, but the tricks enabling structured efficient linear layers (SELLs) have not yet been adapted to the convolutional setting.We present a method to express the weight tensor in a convolutional layer using diagonal matrices, discrete cosine transforms (DCTs) and permutations that can be optimised using standard stochastic gradient methods.A network composed of such structured efficient convolutional layers (SECL) outperforms existing low-rank networks and demonstrates competitive computational efficiency.

可以替换卷积层中的权重矩阵，当作结构化的有效层来训练；表现得和低秩分解一样好。

这项工作将以前的结构化高效线性层应用于凸层，并提出结构化高效卷积层作为原始凸层的替代。


Blind document deblurring is a fundamental task in the field of document processing and restoration, having wide enhancement applications in optical character recognition systems, forensics, etc.Since this problem is highly ill-posed, supervised and unsupervised learning methods are well suited for this application.Using various techniques, extensive work has been done on natural-scene deblurring.However, these extracted features are not suitable for document images.We present SVDocNet, an end-to-end trainable U-Net based spatial recurrent neural network (RNN) for blind document deblurring where the weights of the RNNs are determined by different convolutional neural networks (CNNs).This network achieves state of the art performance in terms of both quantitative measures and qualitative results.

我们提出了SVDocNet，一个基于空间循环神经网络（RNN）的端到端可训练的U-Net，用于盲文档去模糊。


In contrast to the monolithic deep architectures used in deep learning today for computer vision, the visual cortex processes retinal images via two functionally distinct but interconnected networks: the ventral pathway for processing object-related information and the dorsal pathway for processing motion and transformations.Inspired by this cortical division of labor and properties of the magno- and parvocellular systems, we explore an unsupervised approach to feature learning that jointly learns object features and their transformations from natural videos.We propose a new convolutional bilinear sparse coding model that (1) allows independent feature transformations and (2) is capable of processing large images.Our learning procedure leverages smooth motion in natural videos.Our results show that our model can learn groups of features and their transformations directly from natural videos in a completely unsupervised manner.The learned "dynamic filters" exhibit certain equivariance properties, resemble cortical spatiotemporal filters, and capture the statistics of transitions between video frames.Our model can be viewed as one of the first approaches to demonstrate unsupervised learning of primary "capsules" (proposed by Hinton and colleagues for supervised learning) and has strong connections to the Lie group approach to visual perception.

我们扩展了双线性稀疏编码并利用视频序列来学习动态过滤器。


Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) are known to assign lower uncertainty to the OOD data than the target distribution.In this work, we discover that such conventional novelty detection schemes are also vulnerable to the blurred images.Based on the observation, we construct a novel RND-based OOD detector, SVD-RND, that utilizes blurred images during training.Our detector is simple, efficient in test time, and outperforms baseline OOD detectors in various domains.Further results show that SVD-RND learns a better target distribution representation than the baselines.Finally, SVD-RND combined with geometric transform achieves near-perfect detection accuracy in CelebA domain.

我们提出了一种新型的OOD检测器，采用模糊的图像作为对抗性例子。我们的模型在各个领域都取得了显著的OOD检测性能。

本文提出了使用模糊图像作为正则化实例的想法，以提高基于随机网络蒸馏法的分布外检测性能。

本文通过利用应用于数据增量的RND来解决数据外分布问题，通过训练一个模型来匹配有增量输入的随机网络的输出。


Training large deep neural networks on massive datasets is Â computationally very challenging.There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue.The most prominent algorithm in this line of research is LARS, which by Â employing layerwise adaptive learning rates trains ResNet on ImageNet in a few minutes.However, LARS performs poorly for attention models like BERT, indicating that its performance gains are not consistent across tasks.In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches.Using this strategy, we develop a new layerwise adaptive large batch optimization technique called LAMB; we then provide convergence analysis of LAMB as well as LARS, showing convergence to a stationary point in general nonconvex settings.Our empirical results demonstrate the superior performance of LAMB across various tasks such as BERT and ResNet-50 training with very little hyperparameter tuning.In particular, for BERT training, our optimizer enables use of very large batch sizes of 32868 without any degradation of performance. Â By increasing the batch size to the memory limit of a TPUv3 Pod, BERT training time can be reduced from 3 days to just 76 minutes (Table 1).

一个用于一般应用和大批量训练的快速优化器。

在本文中，作者对BERT的大批量训练进行了研究，并在76分钟内成功训练了一个BERT模型。

本文开发了一种分层适应策略，允许用大的32k小批与基线512训练BERT模型。


Model-agnostic meta-learning (MAML) is known as a powerful meta-learning method.However, MAML is notorious for being hard to train because of the existence of two learning rates.Therefore, in this paper, we derive the conditions that inner learning rate $\alpha$ and meta-learning rate $\beta$ must satisfy for MAML to converge to minima with some simplifications.We find that the upper bound of $\beta$ depends on $ \alpha$, in contrast to the case of using the normal gradient descent method.Moreover, we show that the threshold of $\beta$ increases as $\alpha$ approaches its own upper bound.This result is verified by experiments on various few-shot tasks and architectures; specifically, we perform sinusoid regression and classification of Omniglot and MiniImagenet datasets with a multilayer perceptron and a convolutional neural network.Based on this outcome, we present a guideline for determining the learning rates: first, search for the largest possible $\alpha$; next, tune $\beta$ based on the chosen value of $\alpha$.

我们分析了两种学习率在模型诊断性元学习中的收敛作用。 作者通过研究两种学习率，解决了MAML中的优化不稳定问题。 本文研究了一种帮助调整MAML训练算法中使用的两种学习率的方法。


We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures.Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together.The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure.We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference (i.e., thematic fit) and event similarity.The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%.The proposed framework is versatile and holds promise to support learning function-specific representations beyond the SVO structures.

学习相互关联的词组之间联系的任务无关的神经模型。

该论文提出了一种训练特定功能词向量的方法，其中每个词用三个向量表示，每个向量都是不同的类别（主语-动词-宾语）。

本文提出了一个神经网络来学习特函数能的工作表征，并证明了与其他方法相比的优势。


The fabrication of semiconductor involves etching process to remove selected areas from wafers.However, the measurement of etched structure in micro-graph heavily relies on time-consuming manual routines.Traditional image processing usually demands on large number of annotated data and the performance is still poor.We treat this challenge as segmentation problem and use deep learning approach to detect masks of objects in etched structure of wafer.Then, we use simple image processing to carry out automatic measurement on the objects.We attempt Generative Adversarial Network (GAN) to generate more data to overcome the problem of very limited dataset.We download 10 SEM (Scanning Electron Microscope) images of 4 types from Internet, based on which we carry out our experiments.Our deep learning based method demonstrates superiority over image processing approach with  mean accuracy reaching over 96% for the measurements, compared with the ground truth.To the best of our knowledge, it is the first time that deep learning has been applied in semiconductor industry for automatic measurement.

利用深度学习方法对半导体行业的SEM图像进行自动测量


Generating and scheduling activities is particularly challengingwhen considering both consumptive resources andcomplex resource interactions such as time-dependent resourceusage.We present three methods of determining validtemporal placement intervals for an activity in a temporallygrounded plan in the presence of such constraints.We introducethe Max Duration and Probe algorithms which aresound, but incomplete, and the Linear algorithm which issound and complete for linear rate resource consumption.We apply these techniques to the problem of schedulingawakes for a planetary rover where the awake durationsare affected by existing activities.We demonstrate how theProbe algorithm performs competitively with the Linear algorithmgiven an advantageous problem space and well-definedheuristics.We show that the Probe and Linear algorithmsoutperform the Max Duration algorithm empirically.We then empirically present the runtime differences betweenthe three algorithms.The Probe algorithm is currently base-linedfor use in the onboard scheduler for NASAâ€™s next planetaryrover, the Mars 2020 rover.

本文描述并分析了在存在消耗性资源的情况下安排非固定期限活动的三种方法。 本文介绍了在水库资源约束下，行星探测器的车载活动调度的三种方法。


A disentangled representation of a data set should be capable of recovering the underlying factors that generated it.One question that arises is whether using Euclidean space for latent variable models can produce a disentangled representation when the underlying generating factors have a certain geometrical structure.Take for example the images of a car seen from different angles.The angle has a periodic structure but a 1-dimensional representation would fail to capture this topology.How can we address this problem?The submissions presented for the first stage of the  NeurIPS2019 Disentanglement Challenge consist of a Diffusion Variational Autoencoder ($\Delta$VAE) with a hyperspherical latent space which can for example recover periodic true factors.The training of the $\Delta$VAE is enhanced by incorporating a modified version of the Evidence Lower Bound (ELBO) for tailoring the encoding capacity of the posterior approximate.

向NeurIPS2019年基于超球面变异自动编码器的解缠挑战提交的说明


Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence.Recently, classification-based methods were shown to achieve superior results on this task.In this work, we present a unifying view and propose an open-set method to relax current generalization assumptions.Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations.Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types.The strong performance of our method is extensively validated on multiple datasets from different domains.

一种异常检测：使用随机变换分类来泛化到非图像数据。

本文提出了一种用于异常检测的深度方法，该方法统一了最近的深度单类分类和基于转化的分类方法。

本文提出了一种通过使用仿射变换y=Wx+b对一般数据进行基于分类的异常检测的方法。


Recent improvements in large-scale language models have driven progress on automatic generation of syntactically and semantically consistent text for many real-world applications.Many of these advances leverage the availability of large corpora.While training on such corpora encourages the model to understand long-range dependencies in text, it can also result in the models internalizing the social biases present in the corpora.This paper aims to quantify and reduce biases exhibited by language models.Given a conditioning context (e.g. a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sensitive attributes (e.g. country names, occupations, genders, etc.) in the conditioning context, a.k.a. counterfactual evaluation.We quantify these biases by adapting individual and group fairness metrics from the fair machine learning literature.Extensive evaluation on two different corpora (news articles and Wikipedia) shows that state-of-the-art Transformer-based language models exhibit biases learned from data.We propose embedding-similarity and sentiment-similarity regularization methods that improve both individual and group fairness metrics without sacrificing perplexity and semantic similarity---a positive step toward development and deployment of fairer language models for real-world applications.

我们在使用语言模型对文本生成进行反事实评估的基础上，减少情感偏差。

本文测量了由模型生成的文本所反映的语言模型中的情感偏差，并在通常的语言模型目标中加入了其他的目标条款以减少偏差。

本文提出通过使用固定的情感系统来评估预训练的语言模型的偏差，并测试了几种不同的前缀模板。

一种基于语义相似性的方法和一种基于情感相似性的方法，对从大型数据集训练出来的神经语言模型进行去偏差。


Topic modeling of text documents is one of the most important tasks in representation learning.In this work, we propose iTM-VAE, which is a Bayesian nonparametric (BNP) topic model with variational auto-encoders.On one hand, as a BNP topic model, iTM-VAE potentially has infinite topics and can adapt the topic number to data automatically.On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner.Two variants of iTM-VAE are also proposed in this paper, where iTM-VAE-Prod models the generative process in products-of-experts fashion for better performance and iTM-VAE-G places a prior over the concentration parameter such that the model can adapt a suitable concentration parameter to data automatically.Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-arts in terms of perplexity, topic coherence and document retrieval tasks.Moreover, the ability of adjusting the concentration parameter to data is also confirmed by experiments.

一种带有变分自动编码器的贝叶斯非参数主题模型，在困惑度、主题一致性和检索任务方面达到了公共基准的先进水平。

本文通过将Nalisnick & Smith的破棍变分自动编码器与潜在的Dirichlet分配和Miao中使用的几种推理技术相结合，构建了一个具有变分自动编码器的无限主题模型。


Knowledge Distillation (KD) is a widely used technique in recent deep learning research to obtain small and simple models whose performance is on a par with their large and complex counterparts.Standard Knowledge Distillation tends to be time-consuming because of the training time spent to obtain a teacher model that would then provide guidance for the student model.It might be possible to cut short the time by training a teacher model on the fly, but it is not trivial to have such a high-capacity teacher that gives quality guidance to student models this way.To improve this, we present a novel framework of Knowledge Distillation exploiting dark knowledge from the whole training set.In this framework, we propose a simple and effective implementation named Distillation by Utilizing Peer Samples (DUPS) in one generation.We verify our algorithm on numerous experiments.Compared with standard training on modern architectures, DUPS achieves an average improvement of 1%-2% on various tasks with nearly zero extra cost.Considering some typical Knowledge Distillation methods which are much more time-consuming, we also get comparable or even better performance using DUPS.

我们提出了一个新的知识提炼框架，利用同行的样本作为教师的知识蒸馏。

提出了一种通过软化所使用的标签和采用数据集而不是单一样本来提高知识蒸馏的有效性的方法。

本文在最近提出的快照蒸馏技术的基础上，提出用知识蒸馏来解决训练的额外计算成本。


We develop a metalearning approach for learning hierarchically structured poli- cies, improving sample efficiency on unseen tasks through the use of shared primitivesâ€”policies that are executed for large numbers of timesteps.Specifi- cally, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies.We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks.We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies.We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes.We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.

通过对任务分布的端到端训练来学习分层的子策略

作者考虑了学习一套有用的 "子策略 "的问题，这套策略可以在任务之间共享，以便快速开始学习从任务分布中抽取的新任务。

本文提出了一种在专门的多任务设置中诱导时间层次结构的新方法。


This paper proposes a new model for document embedding.Existing approaches either require complex inference or use recurrent neural networks that are difficult to parallelize.We take a different route and use recent advances in language modeling to develop a convolutional neural network embedding model.This allows us to train deeper architectures that are fully parallelizable.Stacking layers together increases the receptive filed allowing each successive layer to model increasingly longer range semantic dependences within the document.Empirically we demonstrate superior results on two publicly available benchmarks.Full code will be released with the final version of this paper.

用于无监督文档嵌入的卷积神经网络模型。

介绍了一种用于诱导文档表征（嵌入）的一般任务的新模型，该模型使用CNN架构来提高计算效率。

本文提出使用具有类似skip-gram目标的CNN作为输出文档嵌入的快速方法。


We prove bounds on the generalization error of convolutional networks.The bounds are in terms of the training loss, the number ofparameters, the Lipschitz constant of the loss and the distance fromthe weights to the initial weights. They are independent of thenumber of pixels in the input, and the height and width of hiddenfeature maps. We present experiments with CIFAR-10, along with varyinghyperparameters of a deep convolutional network, comparing our boundswith practical generalization gaps.

我们证明了考虑到权重配对的卷积神经网络的泛化边界

研究了CNN的泛化能力，并改进了泛化误差的上限，显示了学习型CNN的泛化误差与上限的主导项之间的相关性。

本文基于参数数量、Lipschitz常数和最终权重与初始化的距离，提出了卷积神经网络的泛化边界。


MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years.New applications with stringent real-time requirements in highly constrained devices require further compression of MobileNets-like already computeefficient networks.Model quantization is a widely used technique to compress and accelerate neural network inference and prior works have quantized MobileNets to 4 âˆ’ 6 bits albeit with a modest to significant drop in accuracy.While quantization to sub-byte values (i.e. precision â‰¤ 8 bits) has been valuable, even further quantization of MobileNets to binary or ternary values is necessary to realize significant energy savings and possibly runtime speedups on specialized hardware, such as ASICs and FPGAs.Under the key observation that convolutional filters at each layer of a deep neural network may respond differently to ternary quantization, we propose a novel quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets.The layer-wise hybrid filter banks essentially combine the strengths of full-precision and ternary weight filters to derive a compact, energy-efficient architecture for MobileNets.Using this proposed quantization method, we quantized a substantial portion of weight filters of MobileNets to ternary values resulting in 27.98% savings in energy, and a 51.07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets.

在ImageNet上使用由传统全精度过滤器和三元过滤器组成的混合层，在不损失精度的情况下，模型大小节省了2倍，MobileNets的能量减少了28%。

专注于将MobileNets架构量化为三元值，降低所需空间和计算量，以使神经网络更加节能。

本文提出了层级混合滤波器库，它只将卷积滤波器的一部分量化为三元值，以适应MobileNets架构。


Performing controlled experiments on noisy data is essential in thoroughly understanding deep learning across a spectrum of noise levels.Due to the lack of suitable datasets, previous research have only examined deep learning on controlled synthetic noise, and real-world noise has never been systematically studied in a controlled setting.To this end, this paper establishes a benchmark of real-world noisy labels at 10 controlled noise levels.As real-world noise possesses unique properties, to understand the difference, we conduct a large-scale study across a variety of noise levels and types, architectures, methods, and training settings.Our study shows that: (1) Deep Neural Networks (DNNs) generalize much better on real-world noise.(2) DNNs may not learn patterns first on real-world noisy data.(3) When networks are fine-tuned, ImageNet architectures generalize well on noisy data.(4) Real-world noise appears to be less harmful, yet it is more difficult for robust DNN methods to improve.(5) Robust learning methods that work well on synthetic noise may not work as well on real-world noise, and vice versa.We hope our benchmark, as well as our findings, will facilitate deep learning research on noisy data.

我们建立了一个受控的真实噪声的基准，并揭示了关于真实世界噪声数据的几个有趣的发现。

本文在两种训练环境下比较了6种现有的噪声标签学习方法：从零开始和微调。

作者建立了一个大型的数据集和受控的真实世界噪声的基准，用于对深度学习中的噪声数据进行受控实验。


Designing RNA molecules has garnered recent interest in medicine, synthetic biology, biotechnology and bioinformatics since many functional RNA molecules were shown to be involved in regulatory processes for transcription, epigenetics and translation.Since an RNA's function depends on its structural properties, the RNA Design problem is to find an RNA sequence which satisfies given structural constraints.Here, we propose a new algorithm for the RNA Design problem, dubbed LEARNA.LEARNA uses deep reinforcement learning to train a policy network to sequentially design an entire RNA sequence given a specified target structure.By meta-learning across 65000 different RNA Design tasks for one hour on 20 CPU cores, our extension Meta-LEARNA constructs an RNA Design policy that can be applied out of the box to solve novel RNA Design tasks.Methodologically, for what we believe to be the first time, we jointly optimize over a rich space of architectures for the policy network, the hyperparameters of the training procedure and the formulation of the decision process.Comprehensive empirical results on two widely-used RNA Design benchmarks, as well as a third one that we introduce, show that our approach achieves new state-of-the-art performance on the former while also being orders of magnitudes faster in reaching the previous state-of-the-art performance.In an ablation study, we analyze the importance of our method's different components.

我们学习使用元学习和autoML方法，用强化学习解决RNA设计问题。

使用策略梯度优化来生成折叠成靶二级结构的RNA序列，从而明显提高了准确性和运行时间。


Pruning is a popular technique for compressing a neural network: a large pre-trained network is fine-tuned while connections are successively removed.However, the value of pruning has largely evaded scrutiny.In this extended abstract, we examine residual networks obtained through Fisher-pruning and make two interesting observations.First, when time-constrained, it is better to train a simple, smaller network from scratch than prune a large network.Second, it is the architectures obtained through the pruning process  --- not the learnt weights --- that prove valuable.Such architectures are powerful when trained from scratch.Furthermore, these architectures are easy to approximate without any further pruning: we can prune once and obtain a family of new, scalable network architectures for different memory requirements.

训练小网络胜过修剪，但修剪能找到好的小网络来训练，而且容易复制。


Supervised learning problems---particularly those involving social data---are often subjective.That is, human readers, looking at the same data, might come to legitimate but completely different conclusions based on their personal experiences.Yet in machine learning settings feedback from multiple human annotators is often reduced to a single ``ground truth'' label, thus hiding the true, potentially rich and diverse interpretations of the data found across the social spectrum.We explore the rewards and challenges of discovering and learning representative distributions of the labeling opinions of a large human population.A major, critical cost to this approach is the number of humans needed to provide enough labels not only to obtain representative samples but also to train a machine to predict representative distributions on unlabeled data.We propose aggregating label distributions over, not just individuals, but also data items, in order to maximize the costs of humans in the loop.We test different aggregation approaches on state-of-the-art deep learning models.Our results suggest that careful label aggregation methods can greatly reduce the number of samples needed to obtain representative distributions.

我们研究的问题是学习预测监督学习领域中存在的潜在的信仰多样性。


Recent advancements in deep learning techniques such as Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN) have achieved breakthroughs in the problem of semantic image inpainting, the task of reconstructing missing pixels in given images.While much more effective than conventional approaches, deep learning models require large datasets and great computational resources for training, and inpainting quality varies considerably when training data vary in size and diversity.To address these problems, we present in this paper a inpainting strategy of \textit{Comparative Sample Augmentation}, which enhances the quality of training set by filtering out irrelevant images and constructing additional images using information about the surrounding regions of the images to be inpainted.Experiments on multiple datasets demonstrate that our method extends the applicability of deep inpainting models to training sets with varying sizes, while maintaining inpainting quality as measured by qualitative and quantitative metrics for a large class of deep models, with little need for model-specific consideration.

我们引入了一种战略，可以在不同大小的数据集上对模型进行绘画。

通过使用比较增强型过滤器并在每个像素上添加随机噪声，帮助使用GAN进行图像inpainting。


Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion.Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost.GANs are designed to reach a Nash equilibrium at which each player cannot reduce their cost without changing the other playersâ€™ parameters.One useful approach for the theory of GANs is to show that a divergence between the training distribution and the model distribution obtains its minimum value at equilibrium.Several recent research directions have been motivated by the idea that this divergence is the primary guide for the learning process and that every step of learning should decrease the divergence.We show that this view is overly restrictive.During GAN training, the discriminator provides learning signal in situations where the gradients of the divergences between distributions would not be useful.We provide empirical counterexamples to the view of GAN training as divergence minimization.Specifically, we demonstrate that GANs are able to learn distributions in situations where the divergence minimization point of view predicts they would fail.We also show that gradient penalties motivated from the divergence minimization perspective are equally helpful when applied in other contexts in which the divergence minimization perspective does not predict they would be helpful.This contributes to a growing body of evidence that GAN training may be more usefully viewed as approaching Nash equilibria via trajectories that do not necessarily minimize a specific divergence at each step.

我们发现有证据表明发散最小化可能不是对GAN训练的准确描述。

本报告旨在提出实证性证据，证明发散最小化理论更多的是理解训练GAN的结果的工具，而不是在训练期间必须执行的必要条件。

本文研究了非饱和GAN和两种惩罚性梯度方法的效果，考虑了几个思想实验来证明观察结果，并在真实数据实验上进行了验证。


Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications.Recent works have developed accurate MI estimators through provably low-bias approximations and tight variational lower bounds assuming abundant supply of samples, but require an unrealistic number of samples to guarantee statistical significance of the estimation.In this work, we focus on improving data efficiency and propose a Data-Efficient MINE Estimator (DEMINE) that can provide a tight lower confident interval of MI under limited data, through adding cross-validation to the MINE lower bound (Belghazi et al., 2018).Hyperparameter search is employed and a novel meta-learning approach with task augmentation is developed to increase robustness to hyperparamters, reduce overfitting and improve accuracy.With improved data-efficiency, our DEMINE estimator enables statistical testing of dependency at practical dataset sizes.We demonstrate the effectiveness of DEMINE on synthetic benchmarks and a real world fMRI dataset, with application of inter-subject correlation analysis.

使用神经网络对依赖性进行新的和实用的统计测试，以合成和真实的fMRI数据集为基准。

提出了一种基于神经网络的相互信息估计方法，该方法可以可靠地处理小数据集，通过解耦网络学习问题和估计问题来降低样本复杂性。


Language and vision are processed as two different modal in current work for image captioning.However, recent work on Super Characters method shows the effectiveness of two-dimensional word embedding, which converts text classification problem into image classification problem.In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model.The experimental results on Flickr30k data shows the proposed method gives high quality image captions.An interactive demo is ready to show at the workshop.

使用二维词汇嵌入的图像字幕。


Determining the optimal order in which data examples are presented to Deep Neural Networks during training is a non-trivial problem.However, choosing a non-trivial scheduling method may drastically improve convergence.In this paper, we propose a Self-Paced Learning (SPL)-fused Deep Metric Learning (DML) framework, which we call Learning Embeddings for Adaptive Pace (LEAP).Our method parameterizes mini-batches dynamically based on the \textit{easiness} and \textit{true diverseness} of the sample within a salient feature representation space.In LEAP, we train an \textit{embedding} Convolutional Neural Network (CNN) to learn an expressive representation space by adaptive density discrimination using the Magnet Loss.The \textit{student} CNN classifier dynamically selects samples to form a mini-batch based on the \textit{easiness} from cross-entropy losses and \textit{true diverseness} of examples from the representation space sculpted by the \textit{embedding} CNN.We evaluate LEAP using deep CNN architectures for the task of supervised image classification on MNIST, FashionMNIST, CIFAR-10, CIFAR-100, and SVHN.We show that the LEAP framework converges faster with respect to the number of mini-batch updates required to achieve a comparable or better test performance on each of the datasets.

LEAP将自适应采样的优势与小批量在线学习和自适应表征学习的优势相结合，在端到端DNN训练协议中制定了一个有代表性的自适应策略。

介绍了一种为学生网络创建小型批次的方法，通过使用第二种学习的表征空间，以其 "易用性和真正的多样性 "动态地选择例子。

实验了MNIST、FashionMNIST和CIFAR-10数据集的分类准确率，从而在一个端到端的框架中，用课程学习方式的小型批次选择来学习一个表征。


Conventional deep reinforcement learning typically determines an appropriate primitive action at each timestep, which requires enormous amount of time and effort for learning an effective policy, especially in large and complex environments.To deal with the issue fundamentally, we incorporate macro actions, defined as sequences of primitive actions, into the primitive action space to form an augmented action space.The problem lies in how to find an appropriate macro action to augment the primitive action space.  The agent using a proper augmented action space is able to jump to a farther state and thus speed up the exploration process as well as facilitate the learning procedure.In previous researches, macro actions are developed by mining the most frequently used action sequences or repeating previous actions.However, the most frequently used action sequences are extracted from a past policy, which may only reinforce the original behavior of that policy.On the other hand, repeating actions may limit the diversity of behaviors of the agent.Instead, we propose to construct macro actions by a genetic algorithm, which eliminates the dependency of the macro action derivation procedure from the past policies of the agent.  Our approach appends a macro action to the primitive action space once at a time and evaluates whether the augmented action space leads to promising performance or not.   We perform extensive experiments and show that the constructed macro actions are able to speed up the learning process for a variety of deep reinforcement learning methods.Our experimental results also demonstrate that the macro actions suggested by our approach are transferable among deep reinforcement learning methods and similar environments.We further provide a comprehensive set of ablation analysis to validate our methodology.

我们建议通过遗传算法来构建宏观行动，这就消除了宏观行动推导过程对代理过去策略的依赖性。

本文提出了一种通用算法，通过在原始动作空间中附加一个宏观动作来构建深度强化学习的宏观动作。


A key problem in neuroscience and life sciences more generally is that the data generation process is often best thought of as a hierarchy of dynamic systems.One example of this is in-vivo calcium imaging data, where observed calcium transients are driven by a combination of electro-chemical kinetics where hypothesized trajectories around manifolds determining the frequency of these transients.A recent approach using sequential variational auto-encoders demonstrated it was possible to learn the latent dynamic structure of reaching behaviour from spiking data modelled as a Poisson process.Here we extend this approach using a ladder method to infer the spiking events driving calcium transients along with the deeper latent dynamic system.We show strong performance of this approach on a benchmark synthetic dataset against a number of alternatives.

我们提出了一个对LFADS的扩展，能够推断出尖峰序列，利用层次化的VAE重建钙荧光痕迹。


In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs.There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but they still require a strong cross-lingual signal.In this work, we completely remove the need of parallel data and propose a novel method to train an NMT system in a completely unsupervised manner, relying on nothing but monolingual corpora.Our model builds upon the recent work on unsupervised embedding mappings, and consists of a slightly modified attentional encoder-decoder model that can be trained on monolingual corpora alone using a combination of denoising and backtranslation.Despite the simplicity of the approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014 French-to-English and German-to-English translation.The model can also profit from small parallel corpora, and attains 21.81 and 15.24 points when combined with 100,000 parallel sentences, respectively.Our implementation is released as an open source project.

我们介绍了第一个成功的方法，以无监督的方式训练神经机器翻译，只使用单语语料。

作者提出了一个无监督的NMT模型，该模型不需要两种相关语言的平行语料。

这是一篇关于无监督MT的论文，它只用双语论文和使用单语数据训练的编码器-解码器，在共享嵌入空间中使用词嵌入来训练一个标准架构。


We describe a new training methodology for generative adversarial networks.The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses.This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2.We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10.Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator.Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation.As an additional contribution, we construct a higher-quality version of the CelebA dataset.

我们以渐进的方式训练生成式对抗网络，使我们能够生成高质量的高分辨率图像。

介绍了渐进式增长和一个简单的无参数小型批次总结统计特征，用于GAN训练，以实现高分辨率图像的合成。


Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance.DeepSphere, a method based on a graph representation of the discretized sphere, strikes a controllable balance between these two desiderata.This contribution is twofold.First, we study both theoretically and empirically how equivariance is affected by the underlying graph with respect to the number of pixels and neighbors.Second, we evaluate DeepSphere on relevant problems.Experiments show state-of-the-art performance and demonstrates the efficiency and flexibility of this formulation.Perhaps surprisingly, comparison with previous work suggests that anisotropic filters might be an unnecessary price to pay.

一个基于图形的球形CNN，在各种应用的权衡中取得了有趣的平衡。

结合现有的基于球体离散化为图形的CNN框架，展示了一个与球体上的旋转等价性有关的收敛结果。

作者使用了现有的图CNN表述和一种池化战略，利用球体的分层像素化，从离散的球体中学习。


The notion of the stationary equilibrium ensemble has played a central role in statistical mechanics.In machine learning as well, training serves as generalized equilibration that drives the probability distribution of model parameters toward stationarity.Here, we derive stationary fluctuation-dissipation relations that link measurable quantities and hyperparameters in the stochastic gradient descent algorithm.These relations hold exactly for any stationary state and can in particular be used to adaptively set training schedule.We can further use the relations to efficiently extract information pertaining to a loss-function landscape such as the magnitudes of its Hessian and anharmonicity.Our claims are empirically verified.

我们证明了SGD的波动-消散关系，这可以用来(i)自适应地设置学习率和(ii)探测损失面。

论文的概念在离散时间形式主义中工作，使用主方程，并消除了对损失函数的局部二次近似或对SGD噪声的任何高斯假设的依赖。

作者推导出静止的波动-消散关系，该关系将SGD中的可测量量和超参数联系起来，并利用该关系自适应地设置训练计划和分析损失函数景观。


Recurrent neural networks (RNNs) are difficult to train on sequence processing tasks, not only because input noise may be amplified through feedback, but also because any inaccuracy in the weights has similar consequences as input noise.We describe a method for denoising the hidden state during training to achieve more robust representations thereby improving generalization performance.Attractor dynamics are incorporated into the hidden state to `clean up' representations at each step of a sequence.The attractor dynamics are trained through an auxillary denoising loss to recover previously experienced hidden states from noisy versions of those states.This state-denoised recurrent neural network (SDRNN) performs multiple steps of internal processing for each external sequence step.On a range of tasks, we show that the SDRNN outperforms a generic RNN as well as a variant of the SDRNN with attractor dynamics on the hidden state but without the auxillary loss.We argue that attractor dynamics---and corresponding connectivity constraints---are an essential component of the deep learning arsenal and should be invoked not only for recurrent networks but also for improving deep feedforward nets and intertask transfer.

我们提出了一种对RNN的内部状态进行去噪的机制，以提高泛化性能。


We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system.Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking.Since the state dynamics and rewards depend on the input process, the state alone provides limited information for the expected future returns.Therefore, policy gradient methods with standard state-dependent baselines suffer high variance during training.We derive a bias-free, input-dependent baseline to reduce this variance, and analytically show its benefits over state-dependent baselines.We then propose a meta-learning approach to overcome the complexity of learning a baseline that depends on a long sequence of inputs.Our experimental results show that across environments from queuing systems, computer networks, and MuJoCo robotic locomotion, input-dependent baselines consistently improve training stability and result in better eventual policies.

对于部分由外部输入过程决定的环境，我们推导出一个输入依赖的基线，该基线可证明降低了策略梯度方法的方差，并提高了广泛的RL任务中的策略性能。

作者考虑了在输入驱动环境下的学习问题，显示了PG定理如何仍然适用于输入感知的批评家，并表明输入依赖的基线是在与该批评家的猜想中使用的最佳选择。

本文介绍了RL中策略梯度方法中输入依赖基线的概念，并提出了不同的方法来训练输入依赖基线函数，以帮助清除外部因素扰动的方差。


Deep networks have shown great performance in classification tasks.However, the parameters learned by the classifier networks usually discard stylistic information of the input, in favour of information strictly relevant to classification.We introduce a network that has the capacity to do both classification and reconstruction by adding a "style memory" to the output layer of the network.We also show how to train such a neural network as a deep multi-layer autoencoder, jointly minimizing both classification and reconstruction losses.The generative capacity of our network demonstrates that the combination of style-memory neurons with the classifier neurons yield good reconstructions of the inputs when the classification is correct.We further investigate the nature of the style memory, and how it relates to composing digits and letters.

用样式记忆增强分类器网络的顶层，可以使其具有生成性。

本文提出，训练一个分类器神经网络不仅要进行分类，还要重建其输入的表征，以便从外观上对分类信息进行因子化。

本文提出训练一个自动编码器，使中间层的表示包括输入的类别标签和一个隐藏的向量表示。


Routing models, a form of conditional computation where examples are routed through a subset of components in a larger network, have shown promising results in recent works.Surprisingly, routing models to date have lacked important properties, such as architectural diversity and large numbers of routing decisions.Both architectural diversity and routing depth can increase the representational power of a routing network.In this work, we address both of these deficiencies.We discuss the significance of architectural diversity in routing models, and explain the tradeoffs between capacity and optimization when increasing routing depth.In our experiments, we find that adding architectural diversity to routing models significantly improves performance, cutting the error rates of a strong baseline by 35% on an Omniglot setup.However, when scaling up routing depth, we find that modern routing techniques struggle with optimization.We conclude by discussing both the positive and negative results, and suggest directions for future research.

每个例子的路由模型得益于架构的多样性，但仍然难以扩展到大量的路由决策。

为路由器在每次决策时可用的架构单元类型增加了多样性，并可扩展到更深的网络，在Omniglot上实现最先进的性能。

这项工作扩展了路由网络，使其在整个路由模块中使用多样化的架构


Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs).Although the use of deep-learning techniques has been proposed, the uses have been restricted by the fact the training data are obtained using PDE solvers.Thereby, the uses were limited to domains, where the PDE solver was applicable, but no further. We present methods for training on small domains, while applying the trained models on larger domains, with consistency constraints ensuring the solutions are physically meaningful even at the boundary of the small domains.We demonstrate the results on an air-pollution forecasting model for Dublin, Ireland.

我们提出了用于训练PDE代用模型的RNN，其中一致性约束确保了解决方案在物理上是有意义的，即使训练使用的域比训练的模型小得多。


We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs.Recent theoretical results have shown that optimistic mirror decent (OMD) can enjoy faster regret rates in the context of zero-sum games.WGANs is exactly a context of solving a zero-sum game with simultaneous no-regret dynamics.  Moreover, we show that optimistic mirror decent addresses the limit cycling problem in training WGANs.We formally show that in the case of bi-linear zero-sum games the last iterate of OMD dynamics converges to an equilibrium, in contrast to GD dynamics which are bound to cycle.We also portray the huge qualitative difference between GD and OMD dynamics with toy examples, even when GD is modified with many adaptations proposed in the recent literature, such as gradient penalty or momentum.We apply OMD WGAN training to a bioinformatics problem of generating DNA sequences.We observe that models trained with OMD achieve consistently smaller KL divergence with respect to the true underlying distribution, than models trained with GD variants.Finally, we introduce a new algorithm, Optimistic Adam, which is an optimistic variant of Adam.We apply it to WGAN training on CIFAR10 and observe improved performance in terms of inception score as compared to Adam.

我们提出使用乐观的镜像体面来解决GAN训练中的循环问题。我们还介绍了乐观的亚当算法

本文提出使用乐观的镜像下降法来训练WGAN

本文提出使用乐观的梯度下降法进行GAN训练，避免了SGD及其变体所观察到的循环行为，并在GAN训练中提供了有希望的结果。

本文提出了一个对标准梯度下降的简单修改，声称可以改善GAN和其他最小优化问题的收敛性。


Learning good representations of users and items is crucially important to recommendation with implicit feedback.Matrix factorization is the basic idea to derive the representations of users and items by decomposing the given interaction matrix.However, existing matrix factorization based approaches share the limitation in that the interaction between user embedding and item embedding is only weakly enforced by fitting the given individual rating value, which may lose potentially useful information.In this paper, we propose a novel Augmented Generalized Matrix Factorization (AGMF) approach that is able to incorporate the historical interaction information of users and items for learning effective representations of users and items.Despite the simplicity of our proposed approach, extensive experiments on four public implicit feedback datasets demonstrate that our approach outperforms state-of-the-art counterparts.Furthermore, the ablation study demonstrates that by using multi-hot encoding to enrich user embedding and item embedding for Generalized Matrix Factorization, better performance, faster convergence, and lower training loss can be achieved.

广义矩阵分解的一个简单扩展可以超越最先进的推荐方法。

这项工作提出了一个矩阵分解框架，用于在协作过滤设置中学习用户偏好时强制执行历史数据的效果。


We propose an unsupervised method for building dynamic representations of sequential data, particularly of observed interactions.The method simultaneously acquires representations of input data and its dynamics.It is based on a hierarchical generative model composed of two levels.In the first level, a model learns representations to generate observed data.In the second level, representational states encode the dynamics of the lower one.The model is designed as a Bayesian network with switching variables represented in the higher level, and which generates transition models.The method actively explores the latent space guided by its knowledge and the uncertainty about it.That is achieved by updating the latent variables from prediction error signals backpropagated to the latent space.So, no encoder or inference models are used since the generators also serve as their inverse transformations.The method is evaluated in two scenarios, with static images and with videos.The results show that the adaptation over time leads to better performance than with similar architectures without temporal dependencies, e.g., variational autoencoders.With videos, it is shown that the system extracts the dynamics of the data in states that highly correlate with the ground truth of the actions observed.

一种通过生成模型建立顺序数据及其动态的表征的方法，具有主动的过程

结合神经网络和高斯分布，为图像和视频创建一个架构和生成模型，使生成的图像和提供的图像之间的误差最小。

本文提出一个贝叶斯网络模型，以神经网络的形式实现，以线性动态系统的形式学习不同的数据


Activation is a nonlinearity function that plays a predominant role in the convergence and performance of deep neural networks.While Rectified Linear Unit (ReLU) is the most successful activation function, its derivatives have shown superior performance on benchmark datasets.In this work, we explore the polynomials as activation functions (order â‰¥ 2) that can approximate continuous real valued function within a given interval.Leveraging this property, the main idea is to learn the nonlinearity, accepting that the ensuing function may not be monotonic.While having the ability to learn more suitable nonlinearity, we cannot ignore the fact that it is a challenge to achieve stable performance due to exploding gradients - which is prominent with the increase in order.To handle this issue, we introduce dynamic input scaling, output scaling, and lower learning rate for the polynomial weights.Moreover, lower learning rate will control the abrupt fluctuations of the polynomials between weight updates.In experiments on three public datasets, our proposed method matches the performance of prior activation functions, thus providing insight into a networkâ€™s nonlinearity preference.

我们提出多项式作为激活函数。 作者介绍了以多项式函数为参数的可学习激活函数，并显示出比ReLU略好的结果。