In this work, we focus on the problem of grounding language by training an agent  to follow a set of natural language instructions and navigate to a target object  in a 2D grid environment. The agent receives visual information through raw  pixels and a natural language instruction telling what task needs to be achieved.  Other than these two sources of information, our model does not have any prior  information of both the visual and textual modalities and is end-to-end trainable.  We develop an attention mechanism for multi-modal fusion of visual and textual  modalities that allows the agent to learn to complete the navigation tasks and also  achieve language grounding. Our experimental results show that our attention  mechanism outperforms the existing multi-modal fusion mechanisms proposed in  order to solve the above mentioned navigation task. We demonstrate through the  visualization of attention weights that our model learns to correlate attributes of  the object referred in the instruction with visual representations and also show  that the learnt textual representations are semantically meaningful as they follow  vector arithmetic and are also consistent enough to induce translation between instructions  in different natural languages. We also show that our model generalizes  effectively to unseen scenarios and exhibit zero-shot generalization capabilities.  In order to simulate the above described challenges, we introduce a new 2D environment  for an agent to jointly learn visual and textual modalities

在一个新的可定制的二维网格环境中，通过强化学习来实现基于注意力的language grounding架构

本文解决了给定指令的导航问题，并提出了一种通过注意力机制将文字和视觉信息结合起来的方法。

本文考虑了在先验未知环境的第一人称视角下遵循自然语言指令的问题，并提出了一种神经结构方法。

研究通过遵循给定的自然语言描述和接收原始像素的视觉信息，在二维网格环境中导航到目标对象的问题。


Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\&A architecture called QANet, which does not require recurrent networks:  Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model.   On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8.

一个由卷积和注意力组成的简单架构取得了与有记载的最好的循环模型相同的结果。

基于数据增强的快速高性能释义方法和仅使用卷积和注意力的非循环阅读理解模型。

本文提出应用CNN+自我注意模块代替LSTM，用神经释义模型产生的段落释义加强RC模型训练，以提高RC的性能。

本文提出了一个使用卷积和注意力的阅读理解模型，并建议通过基于现成的神经机器翻译的释义来增加额外的训练数据。


Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective.   In this paper we introduce the building blocks for constructing spherical CNNs. We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant. The spherical correlation satisfies a generalized Fourier theorem, which allows us to compute it efficiently using a generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We demonstrate the computational efficiency, numerical accuracy, and effectiveness of spherical CNNs applied to 3D model recognition and atomization energy regression.

我们介绍了球形CNN，一种针对球形信号的卷积网络，并将其应用于三维模型识别和分子能量回归。

本文提出了一个构建球形卷积网络的框架，该框架基于对几个现有概念的新颖综合

本文重点讨论了如何扩展卷积神经网络，使其具有内置的球形不变性，并采用非阿贝尔谐波分析的工具来实现这一目标。

作者开发了一种新颖的方案，从根本上代表球形数据


We propose a novel method that makes use of deep neural networks and gradient decent to perform automated design on complex real world engineering tasks. Our approach works by training a neural network to mimic the fitness function of a design optimization task and then, using the differential nature of the neural network, perform gradient decent to maximize the fitness. We demonstrate this methods effectiveness by designing an optimized heat sink and both 2D and 3D airfoils that maximize the lift drag ratio under steady state flow conditions. We highlight that our method has two distinct benefits over other automated design approaches. First, evaluating the neural networks prediction of fitness can be orders of magnitude faster then simulating the system of interest. Second, using gradient decent allows the design space to be searched much more efficiently then other gradient free methods. These two strengths work together to overcome some of the current shortcomings of automated design.

一种利用神经网络和梯度下降法对现实世界的物体（如散热器和机翼）进行自动化设计的方法。

神经网络（参数化和预测）和梯度下降（反向传播），为工程任务自动设计。

本文介绍了使用深度网络来逼近复杂物理系统的行为，然后通过优化该网络的输入来设计最佳设备。


Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize with gradient descent and they often do not converge well without careful hyperparameter tuning and proper initialization. We investigate whether turning the adversarial min-max problem into an optimization problem by replacing the maximization part with its dual improves the quality of the resulting alignment and explore its connections to Maximum Mean Discrepancy. Our empirical results suggest that using the dual formulation for the restricted family of linear discriminators results in a more stable convergence to a desirable solution when compared with the performance of a primal min-max GAN-like objective and an MMD objective under the same restrictions. We test our hypothesis on the problem of aligning two synthetic point clouds on a plane and on a real-image domain adaptation problem on digits. In both cases, the dual formulation yields an iterative procedure that gives more stable and monotonic improvement over time.

我们提出了一个用于特征对齐的逻辑对抗距离的双重版本，并表明它比最小最大目标产生更稳定的梯度步骤迭代。

本文讨论了在计算层面固定GAN的问题

本文研究了一种基于逻辑损失上限的对抗性损失的双重表述，并将对抗性训练的标准最小最大问题转化为单一的最小化问题。

建议通过对正则化逻辑回归的最大似然目标进行二元化，将GAN的鞍点目标（用于逻辑回归判别器）重新表述为最小化问题。


There are many applications scenarios for which the computational   performance and memory footprint of the prediction phase of Deep   Neural Networks (DNNs) need to be optimized. Binary Deep Neural   Networks (BDNNs) have been shown to be an effective way of achieving   this objective. In this paper, we show how Convolutional Neural   Networks (CNNs) can be implemented using binary   representations. Espresso is a compact, yet powerful   library written in C/CUDA that features all the functionalities   required for the forward propagation of CNNs, in a binary file less   than 400KB, without any external dependencies. Although it is mainly   designed to take advantage of massive GPU parallelism, Espresso also   provides an equivalent CPU implementation for CNNs. Espresso   provides special convolutional and dense layers for BCNNs,   leveraging bit-packing and bit-wise computations   for efficient execution. These techniques provide a speed-up of   matrix-multiplication routines, and at the same time, reduce memory   usage when storing parameters and activations. We experimentally   show that Espresso is significantly faster than existing   implementations of optimized binary neural networks (~ 2   orders of magnitude). Espresso is released under the Apache 2.0   license and is available at http://github.com/organization/project.

二元神经网络的最先进的计算性能实现

本文介绍了一个用C/CUDA编写的库，它具有BCNN前向传播所需的所有功能。

本文在Binary-NET的基础上，将其扩展到CNN架构，提供了提高前向传递速度的优化，并提供了Binary CNN的优化代码。


Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization. In this paper, we propose a subset selection algorithm that is trainable with gradient based methods yet achieves near optimal performance via submodular optimization. We focus on the task of identifying a relevant set of sentences for claim verification in the context of the FEVER task. Conventional methods for this task look at sentences on their individual merit and thus do not optimize the informativeness of sentences as a set. We show that our proposed method which builds on the idea of unfolding a greedy algorithm into a computational graph allows both interpretability and gradient based training. The proposed differentiable greedy network (DGN) outperforms discrete optimization algorithms as well as other baseline methods in terms of precision and recall.

我们提出了一种子集选择算法，这种算法可以用基于梯度的方法进行训练，但通过子模态优化实现了接近最佳的性能。

提出了一个基于神经网络的模型，该模型通过将基于梯度的优化技术与子模块框架相结合，整合了子模块函数，并命名为 "可微分贪婪网络"（DGN）。

提出了一个旨在选择元素子集的神经网络（例如，从一组检索到的文档中选择与声明主要相关的K句子）。


The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which oftentimes involves cohesive clustering with a focus on instance relations. To overcome the limitations of flat clustering, we introduce hierarchically clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. Specifically, we place a nonparametric Bayesian prior on embeddings to handle dynamic mixture hierarchies under the variational autoencoder framework, and to adopt the generative process of a hierarchical-versioned Gaussian mixture model. Compared with a few prior works focusing on unifying representation learning and hierarchical clustering, HCRL is the first model to consider a generation of deep embeddings from every component of the hierarchy, not just leaf components. This generation process enables more meaningful separations and mergers of clusters via branches in a hierarchy. In addition to obtaining hierarchically clustered embeddings, we can reconstruct data by the various abstraction levels, infer the intrinsic hierarchical structure, and learn the level-proportion features. We conducted evaluations with image and text domains, and our quantitative analyses showed competent likelihoods and the best accuracies compared with the baselines.

我们引入了分层聚类表征学习（HCRL），它同时优化了表征学习和嵌入空间的分层聚类。

本文提出将嵌套的CRP作为聚类模型，而不是主题模型。

提出了一种新的嵌入空间上的分层聚类方法，嵌入空间和分层聚类都是同时学习的。


We introduce a novel geometric perspective and unsupervised model augmentation framework for transforming traditional deep (convolutional) neural networks into adversarially robust classifiers. Class-conditional probability densities based on Bayesian nonparametric mixtures of factor analyzers (BNP-MFA) over the input space are used to design soft decision labels for feature to label isometry. Classconditional distributions over features are also learned using BNP-MFA to develop plug-in maximum a posterior (MAP) classifiers to replace the traditional multinomial logistic softmax classification layers. This novel unsupervised augmented framework, which we call geometrically robust networks (GRN), is applied to CIFAR-10, CIFAR-100, and to Radio-ML (a time series dataset for radio modulation recognition). We demonstrate the robustness of GRN models to adversarial attacks from fast gradient sign method, Carlini-Wagner, and projected gradient descent.

我们为深度神经网络开发了一个统计-几何无监督学习增强框架，使其对对抗性攻击具有稳健性。

利用GRN将传统的深度神经网络转化为对抗性的稳健分析器

提出了一种基于类别条件特征分布的防御措施，将深度神经网络变成稳健的分类器。


Reinforcement learning in environments with large state-action spaces is challenging, as exploration can be highly inefficient. Even if the dynamics are simple, the optimal policy can be combinatorially hard to discover. In this work, we propose a hierarchical approach to structured exploration to improve the sample efficiency of on-policy exploration in large state-action spaces. The key idea is to model a stochastic policy as a hierarchical latent variable model, which can learn low-dimensional structure in the state-action space, and to define exploration by sampling from the low-dimensional latent space. This approach enables lower sample complexity, while preserving policy expressivity. In order to make learning tractable, we derive a joint learning and exploration strategy by combining hierarchical variational inference with actor-critic learning. The benefits of our learning approach are that 1) it is principled, 2) simple to implement, 3) easily scalable to settings with many actions and 4) easily composable with existing deep learning approaches. We demonstrate the effectiveness of our approach on learning a deep centralized multi-agent policy, as multi-agent environments naturally have an exponentially large state-action space. In this setting, the latent hierarchy implements a form of multi-agent coordination during exploration and execution (MACE). We demonstrate empirically that MACE can more efficiently learn optimal policies in challenging multi-agent games with a large number (~20) of agents, compared to conventional baselines. Moreover, we show that our hierarchical structure leads to meaningful agent coordination.

利用具有深度分层策略的结构化探索，使大型状态-动作空间中的深度强化学习更有效率。

一种通过使用具有共享潜伏结构的策略来协调代理行为的方法，一种优化协调策略的变分策略优化方法，以及作者的变分性、层次性更新的推导。

本文提出了一种由分层潜变量组成的算法创新，用于多代理环境下的协调探索。


Much attention has been devoted recently to the generalization puzzle in deep learning: large, deep networks can generalize well, but existing theories bounding generalization error are exceedingly loose, and thus cannot explain this striking performance. Furthermore, a major hope is that knowledge may transfer across tasks, so that multi-task learning can improve generalization on individual tasks. However we lack analytic theories that can quantitatively predict how the degree of knowledge transfer depends on the relationship between the tasks. We develop an analytic theory of the nonlinear dynamics of generalization in deep linear networks, both within and across tasks. In particular, our theory provides analytic solutions to the training and testing error of deep networks as a function of training time, number of examples, network size and initialization, and the task structure and SNR. Our theory reveals that deep networks progressively learn the most important task structure first, so that generalization error at the early stopping time primarily depends on task structure and is independent of network size. This suggests any tight bound on generalization error must take into account task structure, and explains observations about real data being learned faster than random data. Intriguingly our theory also reveals the existence of a learning algorithm that proveably out-performs neural network training through gradient descent. Finally, for transfer learning, our theory reveals that knowledge transfer depends sensitively, but computably, on the SNRs and input feature alignments of pairs of tasks.

我们从理论上可行的线性案例中提供了许多关于神经网络泛化的见解。 作者研究了一个简单的线性网络模型，以了解泛化和迁移学习。


We conduct a mathematical analysis on the Batch normalization (BN) effect on gradient backpropagation in residual network training in this work, which is believed to play a critical role in addressing the gradient vanishing/explosion problem. Specifically, by analyzing the mean and variance behavior of the input and the gradient in the forward and backward passes through the BN and residual branches, respectively, we show that they work together to confine the gradient variance to a certain range across residual blocks in backpropagation. As a result, the gradient vanishing/explosion problem is avoided. Furthermore, we use the same analysis to discuss the tradeoff between depth and width of a residual network and demonstrate that shallower yet wider resnets have stronger learning performance than deeper yet thinner resnets.

批量归一化在整个训练过程中保持梯度方差，从而稳定了优化。 本文分析了批量归一化对残差网络中梯度反向传播的影响


To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the explainable variance in human behavioral judgments. Individual dimensions in the low-dimensional embedding were found to be highly reproducible and interpretable as conveying degrees of taxonomic membership, functionality, and perceptual attributes. We further demonstrated the predictive power of the embeddings for explaining other forms of human behavior, including categorization, typicality judgments, and feature ratings, suggesting that the dimensions reflect human conceptual representations of objects beyond the specific task.

人类的行为判断被用来获得对象的稀疏和可解释的表征，这些表征可以推广到其他任务中去

本文描述了一个关于人类对象/语义表征的大规模实验以及这种表征的模型。

本文通过对从人类对图像进行的独特判断中收集的数据进行训练，开发了一个新的对象表征系统。

一种新的方法来学习稀疏的、积极的、可解释的语义空间，通过训练来专门最大化预测人类的相似性判断。


We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering.    We propose an agent that sits between the user and a black box QA system and learns to reformulate questions to elicit the best possible answers. The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer.    The reformulation system is trained end-to-end to maximize answer quality using policy gradient. We evaluate on SearchQA, a dataset of complex questions extracted from Jeopardy!. The agent outperforms a state-of-the-art base model, playing the role of the environment, and other benchmarks.  We also analyze the language that the agent has learned while interacting with the question answering system. We find that successful question reformulations look quite different from natural language paraphrases. The agent is able to discover non-trivial reformulation strategies that resemble classic information retrieval techniques such as term re-weighting (tf-idf) and stemming.

我们提出了一个位于用户和黑盒问题回答系统之间的代理，它可以学习重新表述问题以获得最佳答案。

本文提出了通过强化学习的方法来主动回答问题，学习以提供最佳答案的方式来重新表述问题。

清楚地描述了研究人员如何设计并积极训练在回答问题的过程中重新表述问题和选择答案的两个模型


Most deep latent factor models choose simple priors for simplicity, tractability  or not knowing what prior to use. Recent studies show that the choice of  the prior may have a profound effect on the expressiveness of the model,  especially when its generative network has limited capacity. In this paper, we propose to learn a proper prior from data for adversarial autoencoders  (AAEs). We introduce the notion of code generators to transform manually selected  simple priors into ones that can better characterize the data distribution. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than  AAEs in both supervised and unsupervised settings. Lastly, we present its  ability to do cross-domain translation in a  text-to-image synthesis task.

为对抗性自动编码器学习优先权

提出了用于条件性图像生成的对抗性自动编码器的简单扩展。

专注于对抗性自动编码器，并介绍了代码生成器网络，将简单的先验转化为与生成器一起能更好地适应数据分布的先验。


In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data. Attempts have been made for utilizing GANs with word embeddings for text generation. This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures. The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.

在生成式对抗网络的帮助下，利用来自Skip-Thought矢量的句子嵌入生成文本。

描述了生成对抗网络的应用，在ski-thought向量的帮助下对文本数据进行建模，并对两个不同的数据集进行了不同风格的GAN实验。


The novel \emph{Unbiased Online Recurrent Optimization} (UORO) algorithm allows for online learning of general recurrent computational graphs such as recurrent network models. It works in a streaming fashion and avoids backtracking through past activations and inputs. UORO is computationally as costly as \emph{Truncated Backpropagation Through Time} (truncated BPTT), a widespread algorithm for online learning of recurrent networks \cite{jaeger2002tutorial}.  UORO is a modification of \emph{NoBackTrack} \cite{DBLP:journals/corr/OllivierC15} that bypasses the need for model sparsity and makes implementation easy in current deep learning frameworks, even for complex models.   Like NoBackTrack, UORO provides unbiased gradient estimates; unbiasedness is the core hypothesis in stochastic gradient descent theory, without which convergence to a local optimum is not guaranteed. On the contrary, truncated BPTT does not provide this property, leading to possible divergence.   On synthetic tasks where truncated BPTT is shown to diverge, UORO converges. For instance, when a parameter has a positive short-term but negative long-term influence, truncated BPTT diverges unless the truncation span is very significantly longer than the intrinsic temporal range of the interactions, while UORO performs well thanks to the unbiasedness of its gradients.

介绍了一种在线的、无偏的、易于实现的循环模型梯度估计。

作者介绍了一种从长序列中在线学习循环神经网络参数的新方法，该方法克服了通过时间来模仿截断反向传播的做法。

本文以一种原则性的方式来处理RNN的在线训练，并提出了对RTRL的修改和使用前向方法来计算梯度。


We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels. This method involves a novel loss function that minimizes the distance between a distribution determined by a set of model outputs and the corresponding distribution given by low-resolution labels over the same set of outputs. This setup does not require that the high-resolution classes match the low-resolution classes and can be used in high-resolution semantic segmentation tasks where high-resolution labeled data is not available. Furthermore, our proposed method is able to utilize both data with low-resolution labels and any available high-resolution labels, which we show improves performance compared to a network trained only with the same amount of high-resolution data.  We test our proposed algorithm in a challenging land cover mapping task to super-resolve labels at a 30m resolution to a separate set of labels at a 1m resolution. We compare our algorithm with models that are trained on high-resolution data and show that 1) we can achieve similar performance using only low-resolution data; and 2) we can achieve better performance when we incorporate a small amount of high-resolution data in our training. We also test our approach on a medical imaging problem, resolving low-resolution probability maps into high-resolution segmentation of lymphocytes with accuracy equal to that of fully supervised models.

将粗略的标签超分辨为像素级标签，应用于航空图像和医学扫描。

如果低分辨率和高分辨率标签的联合分布是已知的，则有一种方法可以超分辨粗略的低分辨率分割标签。


We propose a novel framework for combining datasets via alignment of their associated intrinsic dimensions. Our approach assumes that the two datasets are sampled from a common latent space, i.e., they measure equivalent systems. Thus, we expect there to exist a natural (albeit unknown) alignment of the data manifolds associated with the intrinsic geometry of these datasets, which are perturbed by measurement artifacts in the sampling process. Importantly, we do not assume any individual correspondence (partial or complete) between data points. Instead, we rely on our assumption that a subset of data features have correspondence across datasets. We leverage this assumption to estimate relations between intrinsic manifold dimensions, which are given by diffusion map coordinates over each of the datasets. We compute a correlation matrix between diffusion coordinates of the datasets by considering graph (or manifold) Fourier coefficients of corresponding data features. We then orthogonalize this correlation matrix to form an isometric transformation between the diffusion maps of the datasets. Finally, we apply this transformation to the diffusion coordinates and construct a unified diffusion geometry of the datasets together. We show that this approach successfully corrects misalignment artifacts, and allows for integrated data.

我们提出了一种使用谐波相关的方法来调整从不同数据集学到的潜在特征。

提出利用特征对应关系在同一样本的各批数据之间预先进行流形对齐，以避免收集到噪声测量结果。


Reinforcement learning (RL) has proven to be a powerful paradigm for deriving complex behaviors from simple reward signals in a wide range of environments. When applying RL to continuous control agents in simulated physics environments, the body is usually considered to be part of the environment. However, during evolution the physical body of biological organisms and their controlling brains are co-evolved, thus exploring a much larger space of actuator/controller configurations. Put differently, the intelligence does not reside only in the agent's mind, but also in the design of their body.   We propose a method for uncovering strong agents, consisting of a good combination of a body and policy, based on combining RL with an evolutionary procedure. Given the resulting agent, we also propose an approach for identifying the body changes that contributed the most to the agent performance. We use the Shapley value from cooperative game theory to find the fair contribution of individual components, taking into account synergies between components.   We evaluate our methods in an environment similar to the the recently proposed Robo-Sumo task, where agents in a 3D environment with simulated physics compete in tipping over their opponent or pushing them out of the arena. Our results show that the proposed methods are indeed capable of generating strong agents, significantly outperforming baselines that focus on optimizing the agent policy alone.    A video is available at: www.youtube.com/watch?v=eei6Rgom3YY

在RL控制的代理中进化身体的形状可以提高其性能（并帮助学习）。 纳入Shapley值的PEOM算法，通过识别每个身体部分的贡献来加速进化。


Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never. Even on toy problems, deep reinforcement learners periodically revisit these states, once they are forgotten under a new policy. In this paper, we introduce intrinsic fear, a learned reward shaping that accelerates deep reinforcement learning and guards oscillating policies against periodic catastrophes. Our approach incorporates a second model trained via supervised learning to predict the probability of imminent catastrophe. This score acts as a penalty on the Q-learning objective. Our theoretical analysis demonstrates that the perturbed objective yields the same average return under strong assumptions and an $\epsilon$-close average return under weaker assumptions. Our analysis also shows robustness to classification errors. Equipped with intrinsic fear, our DQNs solve the toy environments and improve on the Atari games Seaquest, Asteroids, and Freeway.

塑造具有内在动机的奖励，以避免灾难性的状态，减轻灾难性的遗忘。

一种RL算法，将DQN算法与平行训练的恐惧模型相结合，以预测灾难性的状态。

本文研究了RL中的灾难性遗忘，通过强调DQN能够学习避免灾难性事件的任务，只要它能避免遗忘。


Convolution is an efficient technique to obtain abstract feature representations using hierarchical layers in deep networks. Although performing convolution in Euclidean geometries is fairly straightforward, its extension to other topological spaces---such as a sphere S^2 or a unit ball B^3---entails unique challenges. In this work, we propose a novel `"volumetric convolution" operation that can effectively convolve arbitrary functions in B^3. We develop a theoretical framework for "volumetric convolution" based on Zernike polynomials and efficiently implement it as a differentiable and an easily pluggable layer for deep networks. Furthermore, our formulation leads to derivation of a  novel formula to measure the symmetry of a function in B^3 around an arbitrary axis, that is useful in 3D shape analysis tasks. We demonstrate the efficacy of proposed volumetric convolution operation on a possible use-case i.e., 3D object recognition task.

一种新的卷积算子用于单位球内的自动表征学习

这项工作与最近的球形CNN和SE(n)等值网络论文有关，并将以前的想法扩展到单位球的体积数据。

提出在卷积网络上使用体积卷积，以学习单元球，并讨论了方法和过程的结果。


Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent’s learning through trial-and-error. For instance, following natural language instructions on the Web (such as booking a flight ticket) leads to RL settings where input vocabulary and number of actionable elements on a page can grow very large. Even though recent approaches improve the success rate on relatively simple environments with the help of human demonstrations to guide the exploration, they still fail in environments where the set of possible instructions can reach millions. We approach the aforementioned problems from a different perspective and propose guided RL approaches that can generate unbounded amount of experience for an agent to learn from. Instead of learning from a complicated instruction with a large vocabulary, we decompose it into multiple sub-instructions and schedule a curriculum in which an agent is tasked with a gradually increasing subset of these relatively easier sub-instructions. In addition, when the expert demonstrations are not available, we propose a novel meta-learning framework that generates new instruction following tasks and trains the agent more effectively. We train DQN, deep reinforcement learning agent, with Q-value function approximated with a novel QWeb neural network architecture on these smaller, synthetic instructions. We evaluate the ability of our agent to generalize to new instructions onWorld of Bits benchmark, on forms with up to 100 elements, supporting 14 million possible instructions. The QWeb agent outperforms the baseline without using any human demonstration achieving 100% success rate on several difficult environments.

我们使用奖励增强、课程学习和元学习来训练强化学习策略，以成功浏览网页。

基于将一条指令分解为多个子指令的理念，开发了一种课程学习方法，用于训练RL代理导航网络。


Labeled text classification datasets are typically only available in a few select languages. In order to train a model for e.g news categorization in a language $L_t$ without a suitable text classification dataset there are two options. The first option is to create a new labeled dataset by hand, and the second option is to transfer label information from an existing labeled dataset in a source language $L_s$ to the target language $L_t$. In this paper we propose a method for sharing label information across languages by means of a language independent text encoder. The encoder will give almost identical representations to multilingual versions of the same text. This means that labeled data in one language can be used to train a classifier that works for the rest of the languages. The encoder is trained independently of any concrete classification task and can therefore subsequently be used for any classification task.   We show that it is possible to obtain good performance even in the case where only a comparable corpus of texts is available.

通过通用编码进行跨语言文本分类

本文提出了一种通过使用可比语料库进行跨语言文本分类的方法。

学习跨语言嵌入并使用源语言中的标记数据训练分类器，以解决在目标语言中没有标记信息的情况下学习跨语言文本分类器的问题


Syntax is a powerful abstraction for language understanding. Many downstream tasks require segmenting input text into meaningful constituent chunks (e.g., noun phrases or entities); more generally, models for learning semantic representations of text benefit from integrating syntax in the form of parse trees (e.g., tree-LSTMs). Supervised parsers have traditionally been used to obtain these trees, but lately interest has increased in unsupervised methods that induce syntactic representations directly from unlabeled text. To this end, we propose the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Unlike many prior approaches, DIORA does not rely on supervision from auxiliary downstream tasks and is thus not constrained to particular domains. Furthermore, competing approaches do not learn explicit phrase representations along with tree structures, which limits their applicability to phrase-based tasks. Extensive experiments on unsupervised parsing, segmentation, and phrase clustering demonstrate the efficacy of our method. DIORA achieves the state of the art in unsupervised parsing (46.9 F1) on the benchmark WSJ dataset.

在这项工作中，我们提出了深度内-外递归自动编码器（DIORA），这是一种完全无监督的发现语法的方法，同时为发现的成分学习表征。

一个用自动编码目标训练的神经潜树模型，在无监督的成分分析方面达到了先进水平，并比其他潜树模型更好地捕捉了句法结构。

本文提出了一个无监督依赖性解析（潜树归纳）的模型，该模型是基于内-外算法与神经建模（递归自动编码器）的结合。


Careful tuning of the learning rate, or even schedules thereof, can be crucial to effective neural net training. There has been much recent interest in gradient-based meta-optimization, where one tunes hyperparameters, or even learns an optimizer, in order to minimize the expected loss when the training procedure is unrolled. But because the training procedure must be unrolled thousands of times, the meta-objective must be defined with an orders-of-magnitude shorter time horizon than is typical for neural net training. We show that such short-horizon meta-objectives cause a serious bias towards small step sizes, an effect we term short-horizon bias. We introduce a toy problem, a noisy quadratic cost function, on which we analyze short-horizon bias by deriving and comparing the optimal schedules for short and long time horizons. We then run meta-optimization experiments (both offline and online) on standard benchmark datasets, showing that meta-optimization chooses too small a learning rate by multiple orders of magnitude, even when run with a moderately long time horizon (100 steps) typical of work in the area. We believe short-horizon bias is a fundamental problem that needs to be addressed if meta-optimization is to scale to practical neural net training regimes.

我们研究了短跨度元优化目标的偏差。 本文提出了一个简化的模型和问题来证明学习率元优化的短跨度偏差。 本文通过对一个玩具问题的一些实验，研究了截断反推法的元优化问题


Mainstream captioning models often follow a sequential structure to generate cap-  tions, leading to issues such as introduction of irrelevant semantics, lack of diversity  in the generated captions, and inadequate generalization performance. In this paper,  we present an alternative paradigm for image captioning, which factorizes the  captioning procedure into two stages: (1) extracting an explicit semantic represen-  tation from the given image; and (2) constructing the caption based on a recursive  compositional procedure in a bottom-up manner. Compared to conventional ones,  our paradigm better preserves the semantic content through an explicit factorization  of semantics and syntax. By using the compositional generation procedure, caption  construction follows a recursive structure, which naturally fits the properties of  human language. Moreover, the proposed compositional procedure requires less  data to train, generalizes better, and yields more diverse captions.

以分层和组合的方式生成标题 本文提出了一种更易解释的图像标题方法。


While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data. Measures for characterizing and monitoring structural properties, however, have not been developed. In this work, we propose neural persistence, a complexity measure for neural network architectures based on topological data analysis on weighted stratified graphs. To demonstrate the usefulness of our approach, we show that neural persistence reflects best practices developed in the deep learning community such as dropout and batch normalization. Moreover, we derive a neural persistence-based stopping criterion that shortens the training process while achieving comparable accuracies as early stopping based on validation loss.

我们为深度神经网络开发了一种新的拓扑复杂性测量方法，并证明它能捕捉到其突出的特性。

本文提出了神经持久性的概念，这是一个拓扑学上的衡量标准，用于给神经网络中的全连接层分配分数。

论文提出利用神经网络的第0持久同源性来分析其复杂性。


Deep neural networks (DNNs) are vulnerable to adversarial examples, which are carefully crafted instances aiming to cause prediction errors for DNNs. Recent research on adversarial examples has examined local neighborhoods in the input space of DNN models. However, previous work has limited what regions to consider, focusing either on low-dimensional subspaces or small balls. In this paper, we argue that information from larger neighborhoods, such as from more directions and from greater distances, will better characterize the relationship between adversarial examples and the DNN models. First, we introduce an attack, OPTMARGIN, which generates adversarial examples robust to small perturbations. These examples successfully evade a defense that only considers a small ball around an input instance. Second, we analyze a larger neighborhood around input instances by looking at properties of surrounding decision boundaries, namely the distances to the boundaries and the adjacent classes. We find that the boundaries around these adversarial examples do not resemble the boundaries around benign examples. Finally, we show that, under scrutiny of the surrounding decision boundaries, our OPTMARGIN examples do not convincingly mimic benign examples. Although our experiments are limited to a few specific attacks, we hope these findings will motivate new, more evasive attacks and ultimately, effective defenses.

观察一个输入周围的决策边界，比固定的小邻域提供更多信息

作者提出了一种新的攻击方法，用于生成对抗性例子，他们攻击通过随机分类L2小扰动创建的分类器

一种对神经网络产生对抗性攻击的新方法，以及一种保护神经网络免受这些攻击的方法。


Machine learning models are usually tuned by nesting optimization of model weights inside the optimization of hyperparameters.   We give a method to collapse this nested optimization into joint stochastic optimization of both weights and hyperparameters.   Our method trains a neural network to output approximately optimal weights as a function of hyperparameters.   We show that our method converges to locally optimal weights and hyperparameters for sufficiently large hypernets.   We compare this method to standard hyperparameter optimization strategies and demonstrate its effectiveness for tuning thousands of hyperparameters.

我们训练一个神经网络来输出近似最佳的权重，作为超参数的函数。 用于神经网络中超参数优化的超网络。


Estimating covariances between financial assets plays an important role in risk management. In practice, when the sample size is small compared to the number of variables, the empirical estimate is known to be very unstable. Here, we propose a novel covariance estimator based on the Gaussian Process Latent Variable Model (GP-LVM). Our estimator can be considered as a non-linear extension of standard factor models with readily interpretable parameters reminiscent of market betas. Furthermore, our Bayesian treatment naturally shrinks the sample covariance matrix towards a more structured matrix given by the prior and thereby systematically reduces estimation errors. Finally, we discuss some financial applications of the GP-LVM model.

用高斯过程潜变量模型对金融资产进行协方差矩阵估计

说明高斯过程潜变量模型（GP-LVM）如何取代经典的线性因子模型来估计投资组合优化问题中的协方差矩阵。

本文使用标准的GPLVMs对S&P500金融时间序列的协方差结构和潜在空间表示进行建模，以优化投资组合和预测缺失值。

本文建议使用GPLVM来建立金融收益模型


We study how, in generative adversarial networks, variance in the discriminator's output affects the generator's ability to learn the data distribution. In particular, we contrast the results from various well-known techniques for training GANs when the discriminator is near-optimal and updated multiple times per update to the generator. As an alternative, we propose an additional method to train GANs by explicitly modeling the discriminator's output as a bi-modal Gaussian distribution over the real/fake indicator variables. In order to do this, we train the Gaussian classifier to match the target bi-modal distribution implicitly through meta-adversarial training. We observe that our new method, when trained together with a strong discriminator, provides meaningful, non-vanishing gradients.

我们引入了元对抗学习，这是一种使GAN正则化的新技术，并提出了一种通过明确控制判别器输出分布的训练方法。

本文提出了用于训练GAN的方差正则化对抗学习，以确保生成器的梯度不会消失。


We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent’s policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights.   NoisyNet is straightforward to implement and adds little computational overhead. We find that replacing the conventional exploration heuristics for A3C, DQN and Dueling agents (entropy reward and epsilon-greedy respectively) with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance.

一个深度强化学习代理，在其权重上添加了参数化的噪音，可以用来帮助高效的探索。

本文介绍了NoisyNets，即参数被参数化噪声函数扰动的神经网络，与基线深度强化学习算法相比，它获得了实质性的性能改进。

通过向深度网络的权重注入噪声，为深度RL提供新的探索方法，噪声有多种形式


Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize efficiently. The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to minimize the number of steps required for localization. Active Neural Localizer is trained end-to-end with reinforcement learning. We use a variety of simulation environments for our experiments which include random 2D mazes, random mazes in the Doom game engine and a photo-realistic environment in the Unreal game engine. The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations. We also show that a model trained on random textures in the Doom environment generalizes well to a photo-realistic office space environment in the Unreal engine.

Active Neural Localizer，一个完全可微的神经网络，利用深度强化学习有效地学习定位。

本文将使用信念网络在已知地图上进行定位的问题表述为一个RL问题，其中代理的目标是最小化自我定位的步骤数。

这是一篇清晰而有趣的论文，它建立了一个参数化的网络，在模拟环境中为机器人选择行动。


Machine translation is an important real-world application, and neural network-based AutoRegressive Translation (ART) models have achieved very promising accuracy. Due to the unparallelizable nature of the autoregressive factorization, ART models have to generate tokens one by one during decoding and thus suffer from high inference latency. Recently, Non-AutoRegressive Translation (NART) models were proposed to reduce the inference time. However, they could only achieve inferior accuracy compared with ART models. To improve the accuracy of NART models, in this paper, we propose to leverage the hints from a well-trained ART model to train the NART model. We define two hints for the machine translation task: hints from hidden states and hints from word alignments, and use such hints to regularize the optimization of NART models. Experimental results show that the NART model trained with hints could achieve significantly better translation performance than previous NART models on several tasks. In particular, for the WMT14 En-De and De-En task, we obtain BLEU scores of 25.20 and 29.52 respectively, which largely outperforms the previous non-autoregressive baselines. It is even comparable to a strong LSTM-based ART model (24.60 on WMT14 En-De), but one order of magnitude faster in inference.

我们开发了一种非自回归机器翻译模型的训练算法，实现了与强自回归基线相当的准确性，但在推理中的速度要快一个数量级。

从中间的隐藏状态和注意权重中提炼知识，以改善非自回归神经机器翻译。

建议利用训练有素的自回归模型，为非自回归神经机器翻译模型的隐藏状态和单词对齐提供信息。


Artificial neural networks are built on the basic operation of linear combination and non-linear activation function. Theoretically this structure can approximate any continuous function with three layer architecture. But in practice learning  the parameters of such network can be hard. Also the choice of activation function can greatly impact the performance of the network. In this paper we are proposing to replace the basic linear combination operation with non-linear operations that do away with the need of additional non-linear activation function. To this end we are proposing the use of elementary  morphological operations (dilation and erosion) as the basic operation in neurons. We show that these networks (Denoted as Morph-Net) with morphological operations can approximate any smooth function requiring less number of parameters than what is necessary for normal neural networks. The results show that our network perform favorably when compared with similar structured network. We have carried out our experiments on  MNIST, Fashion-MNIST, CIFAR10 and CIFAR100.

通过使用形态学操作（扩张和侵蚀），我们定义了一类网络，它可以近似任何连续的函数。

本文提出用扩张和侵蚀操作的组合取代标准的RELU/tanh单元，观察到新的算子创造了更多的超平面，并具有更强的表现力。

作者介绍了Morph-Net，这是一个单层的神经网络，其中的映射是使用形态学的扩张和侵蚀进行的。


With the rapidly scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for efficient deployment. This work aims to advance the compression beyond the weights to the activations of DNNs. We propose the Integral Pruning (IP) technique which integrates the activation pruning with the weight pruning. Through the learning on the different importance of neuron responses and connections, the generated network, namely IPnet, balances the sparsity between activations and weights and therefore further improves execution efficiency. The feasibility and effectiveness of IPnet are thoroughly evaluated through various network models with different activation functions and on different datasets. With <0.5% disturbance on the testing accuracy, IPnet saves 71.1% ~ 96.35% of computation cost, compared to the original dense models with up to 5.8x and 10x reductions in activation and weight numbers, respectively.

这项工作通过整合激活修剪和权重修剪，将DNN的压缩范围从权重扩大到激活。

一种整体模型压缩方法，同时处理权重和激活修剪，实现更有效的网络计算，并有效减少乘积的数量。

本文提出了一种新的方法，通过将激活修剪与权重修剪相结合来降低深度神经网络的计算成本，并表明排他性的权重修剪的常用技术在ReLU后增加了非零激活的数量。


The Variational Auto Encoder (VAE) is a popular generative  latent variable model that is often  applied for representation learning.  Standard VAEs assume continuous valued  latent variables and are trained by maximization of the evidence lower bound (ELBO). Conventional methods obtain a  differentiable estimate of the ELBO with reparametrized sampling and optimize it with Stochastic Gradient Descend (SGD). However, this is not possible if  we want to train VAEs with discrete valued latent variables,  since reparametrized sampling is not possible. Till now, there exist no simple solutions to circumvent this problem.  In this paper, we propose an easy method to train VAEs  with binary or categorically valued latent representations. Therefore, we use a differentiable estimator for the ELBO which is based on importance sampling. In experiments, we verify the approach and train two different VAEs architectures with Bernoulli and  Categorically distributed latent representations on two different benchmark datasets.

我们提出了一种简单的方法来训练具有离散潜在表征的变分自动编码器（VAE），使用重要性采样

引入一个重要性抽样分布，并使用分布中的样本来计算梯度的重要性加权估计值

本文提出使用重要抽样来优化具有离散潜变量的VAE。


Distributed computing can significantly reduce the training time of neural networks. Despite its potential, however, distributed training has not been widely adopted: scaling the training process is difficult, and existing SGD methods require substantial tuning of hyperparameters and learning schedules to achieve sufficient accuracy when increasing the number of workers. In practice, such tuning can be prohibitively expensive given the huge number of potential hyperparameter configurations and the effort required to test each one.       We propose DANA, a novel approach that scales out-of-the-box to large clusters using the same hyperparameters and learning schedule optimized for training on a single worker, while maintaining similar final accuracy without additional overhead. DANA estimates the future value of model parameters by adapting Nesterov Accelerated Gradient to a distributed setting, and so mitigates the effect of gradient staleness, one of the main difficulties in scaling SGD to more workers.   Evaluation on three state-of-the-art network architectures and three datasets shows that DANA scales as well as or better than existing work without having to tune any hyperparameters or tweak the learning schedule. For example, DANA achieves 75.73% accuracy on ImageNet when training ResNet-50 with 16 workers, similar to the non-distributed baseline.

一种新的分布式异步SGD算法，在现有架构上达到了最先进的精度，而没有任何额外的调整或开销。

提出了在中等规模下对现有ASGD方法的改进，即在分布式工人池中使用带有SGD的动量进行异步训练。

本文解决了分布式深度学习训练中的梯度滞后性与并行性能问题，并提出了一种在从站估计未来模型参数以减少通信延迟影响的方法。


This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward and backward propagation processes. We propose a cascaded learning strategy for these local networks. In addition, the approach is also useful from multi-model perspectives, including structural optimization of neural networks, computationally efficient progressive inference, and ensemble classification for performance improvement. Experimental results show the effectiveness of the proposed approach and suggest guidelines for determining appropriate algorithm parameters.

我们提出了一种新的深度神经网络的学习算法，它解开了反向传播的层级依赖性。

DNI的另一种训练范式，其中辅助模块的训练直接近似于原始模型的最终输出，提供附带的好处。

描述了一种没有更新锁定的神经网络训练方法。


\emph{Truncated Backpropagation Through Time} (truncated BPTT, \cite{jaeger2002tutorial}) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of \emph{Backpropagation Through Time} (BPTT \cite{werbos:bptt}) while relieving the need for a complete backtrack through the whole data sequence at every step.   However, truncation favors short-term dependencies: the gradient estimate of truncated BPTT is biased, so that it does not benefit from the convergence guarantees from stochastic gradient theory. We introduce \emph{Anticipated Reweighted Truncated Backpropagation} (ARTBP), an algorithm that keeps the computational benefits of truncated BPTT, while providing unbiasedness. ARTBP works by using variable truncation lengths together with carefully chosen compensation factors in the backpropagation equation. We check the viability of ARTBP on two tasks. First, a simple synthetic task where careful balancing of temporal dependencies at different scales is needed: truncated BPTT displays unreliable performance, and in worst case scenarios, divergence, while ARTBP converges reliably. Second, on Penn Treebank character-level language modelling \cite{ptb_proc}, ARTBP slightly outperforms truncated BPTT.

通过对截断长度的采样和相应的重新加权，提供一个无偏的截断反向传播的版本。 提出了通过时间反向传播中截断点的随机确定方法。 通过时间对反向传播的新近似，以克服在必须从长序列中学习时产生的计算和内存负荷。


Graph convolutional networks (GCNs) have been widely used for classifying graph nodes in the semi-supervised setting.  Previous works have shown that GCNs are vulnerable to the perturbation on adjacency and feature matrices of existing nodes. However, it is unrealistic to change the connections of  existing nodes in many applications, such as existing users in social networks. In this paper, we investigate methods attacking GCNs by adding fake nodes. A greedy algorithm is proposed to generate adjacency and feature matrices of fake nodes, aiming to minimize the classification accuracy on the existing ones. In additional, we introduce a discriminator to classify fake nodes from real nodes, and propose a Greedy-GAN algorithm to simultaneously update the discriminator and the attacker, to make fake nodes indistinguishable to the real ones.   Our non-targeted attack decreases the accuracy of GCN down to 0.10, and our targeted attack reaches a success rate of 0.99 for attacking the whole datasets, and 0.94 on average for attacking a single node.

通过添加假节点对GCN进行非目标和目标攻击
作者提出了一种新的对抗性技术，增加 "假 "节点来欺骗基于GCN的分类器


Transfer learning aims to solve the data sparsity for a specific domain by applying information of another domain. Given a sequence (e.g. a natural language sentence), the transfer learning, usually enabled by recurrent neural network (RNN), represent the sequential information transfer. RNN uses a chain of repeating cells to model the sequence data. However, previous studies of neural network based transfer learning simply transfer the information across the whole layers, which are unfeasible for seq2seq and sequence labeling. Meanwhile, such layer-wise transfer learning mechanisms also lose the fine-grained cell-level information from the source domain.   In this paper, we proposed the aligned recurrent transfer, ART, to achieve cell-level information transfer. ART is in a recurrent manner that different cells share the same parameters. Besides transferring the corresponding information at the same position, ART transfers information from all collocated words in the source domain. This strategy enables ART to capture the word collocation across domains in a more flexible way. We conducted extensive experiments on both sequence labeling tasks (POS tagging, NER) and sentence classification (sentiment analysis). ART outperforms the state-of-the-arts over all experiments.

通过学习使各领域的细胞级信息保持一致，进行序列的迁移学习。

该论文提出使用RNN/LSTM与搭配对齐作为NLP中迁移学习/领域适应的表征学习方法。


Addressing uncertainty is critical for autonomous systems to robustly adapt to the real world. We formulate the problem of model uncertainty as a continuous Bayes-Adaptive Markov Decision Process (BAMDP), where an agent maintains a posterior distribution over latent model parameters given a history of observations and maximizes its expected long-term reward with respect to this belief distribution. Our algorithm, Bayesian Policy Optimization, builds on recent policy optimization algorithms to learn a universal policy that navigates the exploration-exploitation trade-off to maximize the Bayesian value function. To address challenges from discretizing the continuous latent parameter space, we propose a new policy network architecture that encodes the belief distribution independently from the observable state. Our method significantly outperforms algorithms that address model uncertainty without explicitly reasoning about belief distributions and is competitive with state-of-the-art Partially Observable Markov Decision Process solvers.

我们将强化学习中的模型不确定性表述为一个连续的贝叶斯自适应马尔科夫决策过程，并提出了一种实用的、可扩展的贝叶斯策略优化方法。

使用贝叶斯方法，在RL中探索和开发之间有一个更好的权衡。


For many evaluation metrics commonly used as benchmarks for unconditional image generation, trivially memorizing the training set attains a better score than models which are considered state-of-the-art; we consider this problematic.  We clarify a necessary condition for an evaluation metric not to behave this way: estimating the function must require a large sample from the model. In search of such a metric, we turn to neural network divergences (NNDs), which are defined in terms of a neural network trained to distinguish between distributions. The resulting benchmarks cannot be ``won'' by training set memorization, while still being perceptually correlated and computable only from samples. We survey past work on using NNDs for evaluation, implement an example black-box metric based on these ideas, and validate experimentally that it can measure a notion of generalization.

我们认为，GAN基准必须要求从模型中获得大量样本以惩罚记忆，并研究神经网络分歧是否具有这种特性。

作者提出了评价生成式对抗网络所产生的样本质量的标准。


Conventional methods model open domain dialogue generation as a black box through end-to-end learning from large scale conversation data. In this work, we make the first step to open the black box by introducing dialogue acts into open domain dialogue generation. The dialogue acts are generally designed and reveal how people engage in social chat. Inspired by analysis on real data, we propose jointly modeling dialogue act selection and response generation, and perform learning with human-human conversations tagged with a dialogue act classifier and a reinforcement approach to further optimizing the model for long-term conversation. With the dialogue acts, we not only achieve significant improvement over state-of-the-art methods on response quality for given contexts and long-term conversation in both machine-machine simulation and human-machine conversation, but also are capable of explaining why such achievements can be made.

用对话行为生成开放领域的对话

作者使用远距离监督技术，在开放领域的对话中加入对话行为标签，作为生成响应的条件因素

该论文描述了一种将对话行为纳入神经对话代理的技术


We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics. Due to the lack of paired samples and without any definition of the semantic information, the problem might seem ill-posed. Specifically, in typical cases, it seems possible to build infinitely many alternative mappings  from every target mapping. This apparent ambiguity stands in sharp contrast to the recent empirical success in solving this problem.   We identify the abstract notion of aligning two domains in a semantic way with concrete terms of minimal relative complexity. A theoretical framework for measuring the complexity of compositions of functions is developed in order to show that it is reasonable to expect the minimal complexity mapping to be unique. The measured complexity used is directly related to the depth of the neural networks being learned and a semantically aligned mapping could then be captured simply by learning using architectures that are not much bigger than the minimal architecture.   Various predictions are made based on the hypothesis that semantic alignment can be captured by the minimal mapping. These are verified extensively. In addition, a new mapping algorithm is proposed and shown to lead to better mapping results.

我们的假设是，给定两个领域，具有低差异的最低复杂度映射接近于目标映射。

本文讨论了在没有任何监督的情况下学习不同领域之间的映射的问题，陈述了三个猜想。

证明了在非对齐数据的无监督学习中，只用GAN学习域间映射是可能的，没有重建损失。


We present a novel approach for the certification of neural networks against adversarial perturbations which combines scalable overapproximation methods with precise (mixed integer) linear programming. This results in significantly better precision than state-of-the-art verifiers on challenging feedforward and convolutional neural networks with piecewise linear activation functions.

我们用MILP求解器完善了不完全验证器的超近似结果，以证明比最先进的稳健性属性。

介绍了一种验证器，利用超参数化、混合整数线性规划和线性规划松弛，获得了对不完整验证器的精度和完整验证器的可扩展性的改进。

一种混合策略，在具有分段线性激活函数的前馈神经网络的稳健性验证上获得更好的精度，实现了比不完全验证器更好的精度和比完全验证器更大的可扩展性。


A distinct commonality between HMMs and RNNs is that they both learn hidden representations for sequential data. In addition, it has been noted that the backward computation of the Baum-Welch algorithm for HMMs is a special case of the back-propagation algorithm used for neural networks (Eisner (2016)). Do these observations suggest that, despite their many apparent differences, HMMs are a special case of RNNs? In this paper, we show that that is indeed the case, and investigate a series of architectural transformations between HMMs and RNNs, both through theoretical derivations and empirical hybridization. In particular, we investigate three key design factors—independence assumptions between the hidden states and the observation, the placement of softmaxes, and the use of non-linearities—in order to pin down their empirical effects. We present a comprehensive empirical study to provide insights into the interplay between expressivity and interpretability in this model family with respect to language modeling and parts-of-speech induction.

HMM是RNN的一个特例吗？我们通过理论推导和经验杂交研究了HMM和RNN之间的一系列架构转换，并提供了新的见解。

本文探讨了HMM是否是RNN的一个特例，使用语言建模和POS标签


Deep neural networks have been tremendously successful in a number of tasks.  One of the main reasons for this is their capability to automatically  learn representations of data in levels of abstraction,  increasingly disentangling the data as the internal transformations are applied.  In this paper we propose a novel regularization method that penalize covariance between dimensions of the hidden layers in a network, something that benefits the disentanglement.  This makes the network learn nonlinear representations that are linearly uncorrelated, yet allows the model to obtain good results on a number of tasks, as demonstrated by our experimental evaluation.  The proposed technique can be used to find the dimensionality of the underlying data, because it effectively disables dimensions that aren't needed.  Our approach is simple and computationally cheap, as it can be applied as a regularizer to any gradient-based learning model.

我们提出了一种新的正则化方法，对网络中隐藏层的维度之间的协方差进行惩罚。

本文提出了一种正则化机制，该机制对神经网络潜在表征中所有维度之间的协方差进行惩罚，以便拆分潜在表征


This report introduces a training and recognition scheme, in which classification is realized via class-wise discerning. Trained with datasets whose labels are randomly shuffled except for one class of interest, a neural network learns class-wise parameter values, and remolds itself from a feature sorter into feature filters, each of which discerns objects belonging to one of the classes only. Classification of an input can be inferred from the maximum response of the filters. A multiple check with multiple versions of filters can diminish fluctuation and yields better performance. This scheme of discerning, maximum response and multiple check is a method of general viability to improve performance of feedforward networks, and the filter training itself is a promising feature abstraction procedure. In contrast to the direct sorting, the scheme mimics the classification process mediated by a series of one component picking.

所提出的方案模拟了由一系列单组分拣选所调解的分类过程。

一种提高深度网在多类分类任务上的准确性的方法，似乎是通过将多类分类还原为二元分类。

辨别、最大响应和多重检查的新型分类程序，以提高平庸网络的准确性并增强前馈网络。


A long-held conventional wisdom states that larger models train more slowly when using gradient descent. This work challenges this widely-held belief, showing that larger models can potentially train faster despite the increasing computational requirements of each training step. In particular, we study the effect of network structure (depth and width) on halting time and show that larger models---wider models in particular---take fewer training steps to converge.   We design simple experiments to quantitatively characterize the effect of overparametrization on weight space traversal. Results show that halting time improves when growing model's width for three different applications, and the improvement comes from each factor: The distance from initialized weights to converged weights shrinks with a power-law-like relationship, the average step size grows with a power-law-like relationship, and gradient vectors become more aligned with each other during traversal.

经验表明，较大的模型在较少的训练步骤中进行训练，因为权重空间遍历中的所有因素都得到了改善。

本文显示，当应用于NLP问题时，更宽的RNN提高了收敛速度，并延伸出增加深度神经网络中的宽度对优化收敛的影响。

本文描述了过度参数化对算法收敛所需迭代次数的影响，并对神经网络训练中过度参数化的影响做了进一步的实证观察。


Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural networks to learn directly from examples of buggy programs and their fixes. In this work, we consider a recently identified class of bugs called variable-misuse bugs. The state-of-the-art solution for variable misuse enumerates potential fixes for all possible bug locations in a program, before selecting the best prediction. We show that it is beneficial to train a model that jointly and directly localizes and repairs variable-misuse bugs. We present multi-headed pointer networks for this purpose, with one head each for localization and repair. The experimental results show that the joint model significantly outperforms an enumerative solution that uses a pointer based model for repair alone.

用于联合学习的多头指针网络，以定位和修复可变误用的bug

提出了一个基于LSTM的模型，用指针将VarMisuse的问题分解成多个步骤。

本文提出了一个基于LSTM的模型，用于检测和修复VarMisuse的错误，并在几个数据集上展示了与之前的方法相比的显著改进。


Classification and clustering have been studied separately in machine learning and computer vision. Inspired by the recent success of deep learning models in solving various vision problems (e.g., object recognition, semantic segmentation) and the fact that humans serve as the gold standard in assessing clustering algorithms, here, we advocate for a unified treatment of the two problems and suggest that hierarchical frameworks that progressively build complex patterns on top of the simpler ones (e.g., convolutional neural networks) offer a promising solution. We do not dwell much on the learning mechanisms in these frameworks as they are still a matter of debate, with respect to biological constraints. Instead, we emphasize on the compositionality of the real world structures and objects. In particular, we show that CNNs, trained end to end using back propagation with noisy labels, are able to cluster data points belonging to several overlapping shapes, and do so much better than the state of the art algorithms. The main takeaway lesson from our study is that mechanisms of human vision, particularly the hierarchal organization of the visual ventral stream should be taken into account in clustering algorithms (e.g., for learning representations in an unsupervised manner or with minimum supervision) to reach human level clustering performance. This, by no means, suggests that other methods do not hold merits. For example, methods relying on pairwise affinities (e.g., spectral clustering) have been very successful in many cases but still fail in some cases (e.g., overlapping clusters).

用CNN进行类似人类的聚类

该论文验证了深度卷积神经网络可以比其他聚类方法更好地学习对输入数据进行聚类的观点，指出由于视野大，它们有能力解释每个输入点的背景。

这项工作将深度学习的特征表示与类似人类的无监督分组的任务结合起来。


Instancewise feature scoring is a method for model interpretation, which yields, for each test instance, a vector of importance scores associated with features. Methods based on the Shapley score have been proposed as a fair way of computing feature attributions, but incur an exponential complexity in the number of features.   This combinatorial explosion arises from the definition of Shapley value and prevents these methods from being scalable to large data sets and complex models. We focus on settings in which the data have a graph structure, and the contribution of features to the target variable is well-approximated by a graph-structured factorization.   In such settings, we develop two algorithms with linear complexity for instancewise feature importance scoring on black-box models.   We establish the relationship of our methods to the Shapley value and a closely related concept known as the Myerson value from cooperative game theory. We demonstrate on both language and image data that our algorithms compare favorably with other methods using both quantitative metrics and human evaluation.

我们开发了两种线性复杂度算法，用于基于Shapley值的模型诊断模型解释，在这种情况下，特征对目标的贡献被图结构的因子化很好地概括了。

本文提出了用于生成可解释性特征分数的Shapley值的两种近似方法。

本文提出了两种使用Shapely值进行实例重要性评分的方法，并提供了两种在存在已知的特征相关结构时计算近似Shapely值的有效方法。


According to parallel distributed processing (PDP) theory in psychology, neural networks (NN) learn distributed rather than interpretable localist representations. This view has been held so strongly that few researchers have analysed single units to determine if this assumption is correct. However, recent results from psychology, neuroscience and computer science have shown the occasional existence of local codes emerging in artificial and biological neural networks. In this paper, we undertake the first systematic survey of when local codes emerge in a feed-forward neural network, using generated input and output data with known qualities. We find that the number of local codes that emerge from a NN follows a well-defined distribution across the number of hidden layer neurons, with a peak determined by the size of input data, number of examples presented and the sparsity of input data. Using a 1-hot output code drastically decreases the number of local codes on the hidden layer. The number of emergent local codes increases with the percentage of dropout applied to the hidden layer, suggesting that the localist encoding may offer a resilience to noisy networks. This data suggests that localist coding can emerge from feed-forward PDP networks and suggests some of the conditions that may lead to interpretable localist representations in the cortex. The findings highlight how local codes should not be dismissed out of hand.

在前馈神经网络中已经发现了本地代码

一种确定MLP隐藏层中的单个神经元在多大程度上编码局部主义代码的方法，该方法针对不同的输入表征进行了研究。

研究在前馈神经网络的隐蔽层中的本地主义表征的发展。


Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches however primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in relational databases, such as text, images, and numerical values. In our approach, we propose a multimodal embedding using different neural encoders for this variety of data, and combine with existing models to learn embeddings of the entities. We extend existing datasets to create two novel benchmarks, YAGO-10-plus and MovieLens-100k-plus, that contain additional relations such as textual descriptions and images of the original entities. We demonstrate that our model utilizes the additional information effectively to provide further gains in accuracy. Moreover, we test our learned multimodal embeddings by using them to predict missing multimodal attributes.

使用神经编码器扩展关系模型以支持多模态数据。

本文提出通过用多模态信息补充原始实体来进行知识库中的链接预测，并提出了一个能够在对三联体进行评分时编码各种信息的模型。

本文是关于将来自不同模式的信息纳入链接预测方法的研究


An ensemble of neural networks is known to be more robust and accurate than an individual network, however usually with linearly-increased cost in both training and testing.   In this work, we propose a two-stage method to learn Sparse Structured Ensembles (SSEs) for neural networks.  In the first stage, we run SG-MCMC with group sparse priors to draw an ensemble of samples from the posterior distribution of network parameters. In the second stage, we apply weight-pruning to each sampled network and then perform retraining over the remained connections.  In this way of learning SSEs with SG-MCMC and pruning, we not only achieve high prediction accuracy since SG-MCMC enhances exploration of the model-parameter space, but also reduce memory and computation cost significantly in both training and testing of NN ensembles.  This is thoroughly evaluated in the experiments of learning SSE ensembles of both FNNs and LSTMs.  For example, in LSTM based language modeling (LM), we obtain 21\% relative reduction in LM perplexity by learning a SSE of 4 large LSTM models, which has only 30\% of model parameters and 70\% of computations in total, as compared to the baseline large LSTM LM.  To the best of our knowledge, this work represents the first methodology and empirical study of integrating SG-MCMC, group sparse prior and network pruning together for learning NN ensembles.

提出一种新的方法，通过整合SG-MCMC采样、分组稀疏先验和网络修剪来学习稀疏结构化集合（SSE），与传统方法相比，性能提高，成本大幅降低。

作者提出了一个生成稀疏结构模型集合的程序

一个用于训练集合神经网络的新框架，在深度学习中使用SG-MCMC方法，然后通过分组稀疏+修剪提高计算效率。

本文探讨了使用FNN和LSTM来使贝叶斯模型平均化在计算上更加可行，并提高平均模型性能。


This paper introduces a new framework for data efficient and versatile learning. Specifically:  1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods.   2) We introduce \Versa{}, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. \Versa{} substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training.  3) We evaluate \Versa{} on benchmark datasets where the method sets new state-of-the-art results, and can handle arbitrary number of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task.

新的元学习框架，统一并扩展了广泛的现有少样本学习方法。在不需要迭代测试时间推导的情况下，在少数学习基准上取得了强大的性能。

这项工作从概率推理的角度解决了少许学习的问题，尽管比许多竞争者的设置更简单，但仍达到了最先进的水平。


In recent years, softmax together with its fast approximations has become the de-facto loss function for deep neural networks with multiclass predictions. However, softmax is used in many problems that do not fully fit the multiclass framework and where the softmax assumption of mutually exclusive outcomes can lead to biased results. This is often the case for applications such as language modeling, next event prediction and matrix factorization, where many of the potential outcomes are not mutually exclusive, but are more likely to be independent conditionally on the state. To this end, for the set of problems with positive and unlabeled data, we propose a relaxation of the original softmax formulation, where, given the observed state, each of the outcomes are conditionally independent but share a common set of negatives. Since we operate in a regime where explicit negatives are missing, we create an adversarially-trained model of negatives and derive a new negative sampling and weighting scheme which we denote as Cooperative Importance Sampling (CIS). We show empirically the advantages of our newly introduced negative sampling scheme by pluging it in the Word2Vec algorithm and benching it extensively against other negative sampling schemes on both language modeling and matrix factorization tasks and show large lifts in performance.

为正向数据定义部分互斥的softmax损失并实施基于合作的采样方案

本文提出了合作重要性抽样，以解决传统softmax的互斥假设在没有明确定义负样本时有偏差的问题。

本文提出了PMES方法来放松softmax loss中的排他性结果假设，证明了在改进word2vec类型的嵌入模型方面的实证价值。


Over the past few years, various tasks involving videos such as classification, description, summarization and question answering have received a lot of attention. Current models for these tasks compute an encoding of the video by treating it as a sequence of images and going over every image in the sequence, which becomes computationally expensive for longer videos. In this paper, we focus on the task of video classification and aim to reduce the computational cost by using the idea of distillation. Specifically, we propose a Teacher-Student network wherein the teacher looks at all the frames in the video but the student looks at only a small fraction of the frames in the video. The idea is to then train the student to minimize  (i)  the difference between the final representation computed by the student and the teacher and/or (ii) the difference between the distributions predicted by the teacher and the student. This smaller student network which involves fewer computations but still learns to mimic the teacher can then be employed at inference time for video classification. We experiment with the YouTube-8M dataset and show  that the proposed student network can reduce the inference time by upto 30% with a negligent drop in the performance.

使用较少帧的高效视频分类的师生框架

本文提出了一个想法，从一个完整的视频分类模型中提炼出一个只接收较少帧数的小型模型。

作者提出了一个解决视频分类问题的师生网络，提出了旨在降低计算成本的串行和并行训练算法。


Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to transfer techniques across research lines in a principled way. For example, we apply the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism that leverages generated samples. Experiments show generality and effectiveness of the transfered techniques.

深度生成模型大类的统一统计视图

本文建立了一个框架，将GAN算法解释为对生成模型进行一种变分推理，重构一个样本是否来自生成数据分布的真实的指标变量。


Deep neural networks have demonstrated promising prediction and classification performance on many healthcare applications. However, the interpretability of those models are often lacking. On the other hand, classical interpretable models such as rule lists or decision trees do not lead to the same level of accuracy as deep neural networks and can often be too complex to interpret (due to the potentially large depth of rule lists). In this work, we present PEARL,  Prototype lEArning via Rule Lists, which iteratively uses rule lists to guide a neural network to learn representative data prototypes. The resulting prototype neural network provides  accurate prediction, and the prediction can be easily explained by  prototype and its guiding rule lists. Thanks to the prediction power of neural networks, the rule lists from				 prototypes are more concise and hence provide better interpretability. On two real-world electronic healthcare records (EHR) datasets, PEARL consistently outperforms all baselines across both datasets, especially achieving performance improvement over conventional rule learning by up to 28% and over prototype learning by up to 3%. Experimental results also show the resulting interpretation of PEARL is  simpler than the standard rule learning.

一种结合规则列表学习和原型学习的方法

提出了一个新的可解释性预测框架，它结合了基于规则的学习、原型学习和NN，特别适用于纵向数据。

本文旨在解决深度学习模型缺乏可解释性的问题，并提出了通过规则列表的原型学习（PEARL），它结合了规则学习和原型学习，以实现更准确的分类，并使可解释性的任务更加简单。


Generative Adversarial Networks (GANs) are powerful tools for realistic image generation. However, a major drawback of GANs is that they are especially hard to train, often requiring large amounts of data and long training time. In this paper we propose the Deli-Fisher GAN, a GAN that generates photo-realistic images by enforcing structure on the latent generative space using similar approaches in \cite{deligan}. The structure of the latent space we consider in this paper is modeled as a mixture of Gaussians, whose parameters are learned in the training process. Furthermore, to improve stability and efficiency, we use the Fisher Integral Probability Metric as the divergence measure in our GAN model, instead of the Jensen-Shannon divergence. We show by experiments that the Deli-Fisher GAN performs better than DCGAN, WGAN, and the Fisher GAN as measured by inception score.

本文提出了一种新的生成对抗网络，它比现有的生成对抗网络更稳定、更有效，并能产生更好的图像。

本文结合了Deli-GAN和Fisher GAN，前者在潜空间中具有混合先验分布，后者使用Fisher IPM而不是JSD作为目标。


Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal and spatial attentional mechanisms into neural networks increases the performance of the system substantially. We report on a new modular network architecture that applies an attentional mechanism not on temporal and spatial regions of the input, but on sensor selection for multi-sensor setups. This network called the sensor transformation attention network (STAN) is evaluated in scenarios which include the presence of natural noise or synthetic dynamic noise. We demonstrate how the attentional signal responds dynamically to changing noise levels and sensor-specific noise, leading to reduced word error rates (WERs) on both audio and visual tasks using TIDIGITS and GRID; and also on CHiME-3, a multi-microphone real-world noisy dataset. The improvement grows as more channels are corrupted as demonstrated on the CHiME-3 dataset. Moreover, the proposed STAN architecture naturally introduces a number of advantages including ease of removing sensors from existing architectures, attentional interpretability, and increased robustness to a variety of noise environments.

我们引入了一个带有注意力机制的模块化多传感器网络架构，它能够在CHiME-3的真实世界的噪声数据上进行动态传感器选择。

一个通用的神经结构能够学习根据每个传感器的相对质量而必须对不同的输入通道给予的注意力。

考虑使用注意力来选择传感器或通道，在TIDIGITS和GRID上的结果显示了注意力比特征串联的好处。


Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based training schemes provide beneficial services but suffer from potential privacy risks due to excessive user data collection. To enable cloud-based DNN training while protecting the data privacy simultaneously, we propose to leverage the intermediate representations of the data, which is achieved by splitting the DNNs and deploying them separately onto local platforms and the cloud. The local neural network (NN) is used to generate the feature representations. To avoid local training and protect data privacy, the local NN is derived from pre-trained NNs. The cloud NN is then trained based on the extracted intermediate representations for the target learning task. We validate the idea of DNN splitting by characterizing the dependency of privacy loss and classification accuracy on the local NN topology for a convolutional NN (CNN) based image classification task. Based on the characterization, we further propose PrivyNet to determine the local NN topology, which optimizes the accuracy of the target learning task under the constraints on privacy loss, local computation, and storage. The efficiency and effectiveness of PrivyNet are demonstrated with CIFAR-10 dataset.

为了实现基于云的DNN训练，同时保护数据隐私，我们建议利用中间数据表征，这是通过拆分DNN并将其分别部署到本地平台和云端来实现的。

本文提出了一种通过学习特征表示来实现数据私有化的技术，这种技术很难用于图像重建，但对图像分类有帮助。


Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their application to medical data. RGANs make use of recurrent neural networks (RNNs) in the generator and the discriminator. In the case of RCGANs, both of these RNNs are conditioned on auxiliary information. We demonstrate our models in a set of toy datasets, where we show visually and quantitatively (using sample likelihood and maximum mean discrepancy) that they can successfully generate realistic time-series. We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa. We illustrate with these metrics that RCGANs can generate time-series data useful for supervised training, with only minor degradation in performance on real test data. This is demonstrated on digit classification from ‘serialised’ MNIST and by training an early warning system on a medical dataset of 17,000 patients from an intensive care unit. We further discuss and analyse the privacy concerns that may arise when using RCGANs to generate realistic synthetic medical time series data, and demonstrate results from differentially private training of the RCGAN.

用于实值医疗序列生成的条件循环GAN，显示了新的评估方法和实证性的隐私分析。

建议使用GAN生成的合成数据来替代个人身份识别数据，用于训练对隐私敏感的应用的ML模型

作者提出了一种新型的循环GAN架构，可以生成连续的领域序列，并在几个合成任务和一个ICU时间序列数据任务上对其进行了评估。

提议使用RGAN和RCGAN来生成实际数据的合成序列。


Emphasis effects – visual changes that make certain elements more  prominent – are commonly used in information visualization to draw  the user’s attention or to indicate importance. Although theoretical  frameworks of emphasis exist (that link visually diverse emphasis  effects through the idea of visual prominence compared to background  elements), most metrics for predicting how emphasis effects  will be perceived by users come from abstract models of human  vision which may not apply to visualization design. In particular,  it is difficult for designers to know, when designing a visualization,  how different emphasis effects will compare and what level of one  effect is equivalent to what level of another. To address this gap,  we carried out two studies that provide empirical evidence about  how users perceive different emphasis effects, using three visual  variables (colour, size, and blur/focus) and eight strength levels.  Results from gaze tracking, mouse clicks, and subjective responses  show that there are significant differences between visual variables  and between levels, and allow us to develop an initial understanding  of perceptual equivalence. We developed a model from the data in  our first study, and used it to predict the results in the second; the  model was accurate, with high correlations between predictions and  real values. Our studies and empirical models provide valuable new  information for designers who want to understand and control how  emphasis effects will be perceived by users.

我们的研究和实证模型为那些希望了解和控制强调效果如何被用户感知的设计者提供了宝贵的新信息。

本文考虑了在数据可视化中哪种视觉高亮被认为是更快的，以及不同的高亮方法如何相互比较

两项关于强调效果的研究，一项是评估有用的差异水平，一项是使用实际不同的可视化的更多应用，以进行更生态有效的调查。


Memory Network based models have shown a remarkable progress on the task of relational reasoning.  Recently, a simpler yet powerful neural network module called Relation Network (RN) has been introduced.   Despite its architectural simplicity, the time complexity of relation network grows quadratically with data, hence limiting its application to tasks with a large-scaled memory.  We introduce Related Memory Network, an end-to-end neural network architecture exploiting both memory network and relation network structures.   We follow memory network's four components while each component operates similar to the relation network without taking a pair of objects.   As a result, our model is as simple as RN but the computational complexity is reduced to linear time.  It achieves the state-of-the-art results in jointly trained bAbI-10k story-based question answering and  bAbI dialog dataset.

基于记忆网络（MemNN）和关系网络（RN）的简单推理架构，与RN相比，降低了时间复杂度，在基于bAbI故事的QA和bAbI对话上取得了最先进的成果。

介绍了相关记忆网络（RRMN），这是关系网络（RN）的改进。


We investigate in this paper the architecture of deep convolutional networks. Building on existing state of the art models, we propose a reconfiguration of the model parameters into several parallel branches at the global network level, with each branch being a standalone CNN. We show that this arrangement is an efficient way to significantly reduce the number of parameters while at the same time improving the performance. The use of branches brings an additional form of regularization. In addition to splitting the parameters into parallel branches, we propose a tighter coupling of these branches by averaging their log-probabilities. The tighter coupling favours the learning of better representations, even at the level of the individual branches, as compared to when each branch is trained independently. We refer to this branched architecture as "coupled ensembles". The approach is very generic and can be applied with almost any neural network architecture. With coupled ensembles of DenseNet-BC and parameter budget of 25M, we obtain error rates of 2.92%, 15.68% and 1.50% respectively on CIFAR-10, CIFAR-100 and SVHN tasks. For the same parameter budget, DenseNet-BC has an error rate of 3.46%, 17.18%, and 1.8% respectively.   With ensembles of coupled ensembles, of DenseNet-BC networks, with 50M total parameters, we obtain error rates of 2.72%, 15.13% and 1.42% respectively on these tasks.

我们表明，将一个神经网络分割成平行分支可以提高性能，而分支的适当耦合可以进一步提高性能。

这项工作提出了一个使用新的分支结构对现有的最先进的CNN模型进行重新配置，具有更好的性能。

本文展示了耦合合集的参数节约优势。

提出了一个深度网络架构，该架构使用多个平行分支处理数据，并结合这些分支的后验结果来计算最终得分。


Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few. Though deep learning leads to groundbreaking performance in these domains, the networks used are very demanding computationally and are far from real-time even on a GPU, which is not power efficient and therefore does not suit low power systems such as mobile devices. To overcome this challenge, some solutions have been proposed for quantizing the weights and activations of these networks, which accelerate the runtime significantly. Yet, this acceleration comes at the cost of a larger error. The NICE method proposed in this work trains quantized neural networks by noise injection and a learned clamping, which improve the accuracy. This leads to state-of-the-art results on various regression and classification tasks, e.g., ImageNet classification with architectures such as ResNet-18/34/50 with low as 3-bit weights and 3 -bit activations. We implement the proposed solution on an FPGA to demonstrate its applicability for low power real-time applications.

结合噪声注入、渐进式量化和激活钳制学习，实现最先进的3、4和5位量化

建议在训练过程中注入噪声，并在神经网络量化中钳制某一层的参数值以及激活输出。

一种用于分类和回归的深度神经网络的量化方法，使用噪声注入、用学习到的最大激活量进行箝制，以及渐进式块状量化，使其性能与最先进的方法相当或更好。


In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at at higher level of abstraction is needed. We propose Leap, a framework that achieves this by transferring knowledge across learning processes. We associate each task with a manifold on which the training process travels from initialization to final parameters and construct a meta-learning objective that minimizes the expected length of this path. Our framework leverages only information obtained during training and can be computed on the fly at negligible cost. We demonstrate that our framework outperforms competing methods, both in meta-learning and transfer learning, on a set of computer vision tasks. Finally, we demonstrate that Leap can transfer knowledge across learning processes in demanding reinforcement learning environments (Atari) that involve millions of gradient steps.

我们提出了Leap，一个通过最小化训练过程在任务损失表面上的预期距离来转移学习过程中的知识的框架。

文章提出了一个新的元学习目标，旨在处理表现出大量任务间多样性的任务集合时，超越最先进的方法。


Given an existing trained neural network, it is often desirable to learn new capabilities without hindering performance of those already learned. Existing approaches either learn sub-optimal solutions, require joint training, or incur a substantial increment in the number of parameters for each added task, typically as many as the original network. We propose a method called Deep Adaptation Networks (DAN) that constrains newly learned filters to be linear combinations of existing ones. DANs preserve performance on the original task, require a fraction (typically 13%) of the number of parameters compared to standard fine-tuning procedures and converge in less cycles of training to a comparable or better level of performance. When coupled with standard network quantization techniques, we further reduce the parameter cost to around 3% of the original with negligible or no loss in accuracy. The learned architecture can be controlled to switch between various learned representations, enabling a single network to solve a task from multiple different domains. We conduct extensive experiments showing the effectiveness of our method on a range of image classification tasks and explore different aspects of its behavior.

一种替代迁移学习的方法，学习速度更快，需要的参数少得多（3-13%），通常能取得更好的结果，并且精确地保留了旧任务的性能。

用于图像分类数据集的增量学习的控制器模块


High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications. This paper presents a general technique toward 8-bit low precision inference of convolutional neural networks, including 1) channel-wise scale factors of weights, especially for depthwise convolution, 2) Winograd convolution, and 3) topology-wise 8-bit support. We experiment the techniques on top of a widely-used deep learning framework. The 8-bit optimized model is automatically generated with a calibration process from FP32 model without the need of fine-tuning or retraining. We perform a systematical and comprehensive study on 18 widely-used convolutional neural networks and demonstrate the effectiveness of 8-bit low precision inference across a wide range of applications and use cases, including image classification, object detection, image segmentation, and super resolution. We show that the inference throughput  and latency are improved by 1.6X and 1.5X respectively with minimal within 0.6%1to no loss in accuracy from FP32 baseline. We believe the methodology can provide the guidance and reference design of 8-bit low precision inference for other frameworks. All the code and models will be publicly available soon.

我们提出了一种对卷积神经网络进行8位低精度推理的通用技术。 本文设计了一个系统来自动量化CNN预训练的模型


Recent approaches have successfully demonstrated the benefits of learning the parameters of shallow networks in hyperbolic space. We extend this line of work by imposing hyperbolic geometry on the embeddings used to compute the ubiquitous attention mechanisms for different neural networks architectures. By only changing the geometry of embedding of object representations, we can use the embedding space more efficiently without increasing the number of parameters of the model. Mainly as the number of objects grows exponentially for any semantic distance from the query, hyperbolic geometry  --as opposed to Euclidean geometry-- can encode those objects without having any interference. Our method shows improvements in generalization on neural machine translation on WMT'14 (English to German), learning on graphs (both on synthetic and real-world graph tasks) and visual question answering (CLEVR) tasks while keeping the neural representations compact.

我们建议加入来自双曲几何的归纳偏差和操作，以改善神经网络的注意力机制。

本文用负双曲线距离取代了注意力机制中使用的点积相似性，并将其应用于现有的Transformer模型、图形注意力网络和关系网络中。

作者提出了一种新的方法，通过改变匹配和聚合函数，使用双曲线几何来改善关系-注意力。


We present a method for evaluating the sensitivity of deep reinforcement learning (RL) policies. We also formulate a zero-sum dynamic game for designing robust deep reinforcement learning policies. Our approach mitigates the brittleness of policies when agents are trained in a simulated environment and are later exposed to the real world where it is hazardous to employ RL policies. This framework for training deep RL policies involve a zero-sum  dynamic game against an adversarial agent, where the goal is to drive the system dynamics to a saddle region. Using a variant of the guided policy search algorithm, our agent learns to adopt robust policies that require less samples for learning the dynamics and performs better than the GPS algorithm. Without loss of generality, we demonstrate that deep RL policies trained in this fashion will be maximally robust to a ``worst" possible adversarial disturbances.

本文展示了H-无穷大控制理论如何帮助更好地为机器人电机设计鲁稳健的深度策略。

建议将稳健控制的要素纳入指导性策略研究，以便设计出一种对扰动和模型不匹配具有弹性的方法。

本文提出了一种评估深度RL策略的敏感性和稳健性的方法，并提出了一种学习稳健策略的动态博弈方法。


Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we provide a quantitative analysis of the robustness of classifiers to universal perturbations, and draw a formal link between the robustness to universal perturbations, and the geometry of the decision boundary. Specifically, we establish theoretical bounds on the robustness of classifiers under two decision boundary models (flat and curved models). We show in particular that the robustness of deep networks to universal perturbations is driven by a key property of their curvature: there exist shared directions along which the decision boundary of deep networks is systematically positively curved. Under such conditions, we prove the existence of small universal perturbations. Our analysis further provides a novel geometric method for computing universal perturbations, in addition to explaining their properties.

分析分类器对普遍扰动的脆弱性以及与决策边界曲率的关系。

本文提供了一个有趣的分析，将分类器决策边界的几何形状与小型通用对抗性扰动联系起来。

本文讨论了通用扰动--如果将其添加到大多数输入数据点中，就会误导经过训练的分类器。

本文开发了一些模型，试图解释愚弄神经网络的普遍扰动的存在。


Behavioral skills or policies for autonomous agents are conventionally learned from reward functions, via reinforcement learning, or from demonstrations, via imitation learning. However, both modes of task specification have their disadvantages: reward functions require manual engineering, while demonstrations require a human expert to be able to actually perform the task in order to generate the demonstration. Instruction following from natural language instructions provides an appealing alternative: in the same way that we can specify goals to other humans simply by speaking or writing, we would like to be able to specify tasks for our machines. However, a single instruction may be insufficient to fully communicate our intent or, even if it is, may be insufficient for an autonomous agent to actually understand how to perform the desired task. In this work, we propose an interactive formulation of the task specification problem, where iterative language corrections are provided to an autonomous agent, guiding it in acquiring the desired skill. Our proposed language-guided policy learning algorithm can integrate an instruction and a sequence of corrections to acquire new skills very quickly. In our experiments, we show that this method can enable a policy to follow instructions and corrections for simulated navigation and manipulation tasks, substantially outperforming direct, non-interactive instruction following.

我们提出了一种元学习方法，用于用自然语言互动地纠正策略。

本文提供了一个元学习框架，展示了如何在互动设置中学习新的任务。每个任务都是通过强化学习设置学习的，然后通过观察新的指令来更新任务。

本文通过自然语言指令，在一个反复的过程中教代理完成任务。


Deep generative models such as Generative Adversarial Networks (GANs) and  Variational Auto-Encoders (VAEs) are important tools to capture and investigate  the properties of complex empirical data. However, the complexity of their inner  elements makes their functionment challenging to assess and modify. In this  respect, these architectures behave as black box models. In order to better  understand the function of such networks, we analyze their modularity based on  the counterfactual manipulation of their internal variables. Our experiments on the  generation of human faces with VAEs and GANs support that modularity between  activation maps distributed over channels of generator architectures is achieved  to some degree, can be used to better understand how these systems operate and allow meaningful transformations of the generated images without further training.  erate and edit the content of generated images.

我们研究了深度生成模型的模块化。

本文提供了一种研究深度生成模型的模块化结构的方法，其关键概念是分布在生成器架构的通道上。