We introduce an analytic distance function for moderately sized point sets of known cardinality that is shown to have very desirable properties, both as a loss function as well as a regularizer for machine learning applications. We compare our novel construction to other point set distance functions and show proof of concept experiments for training neural networks end-to-end on point set prediction tasks such as object detection.

点集预测的排列不变损失函数。

提出了一种新的点注册损失（对准两个点集），具有较好的排列不变的特性。

本文介绍了一个新的点集之间的距离函数，在一个端到端的物体检测任务中应用了另外两个排列距离，并表明在二维中，全息损失的所有局部最小值都是全局最小值。

提出了取决于集合距离的排列不变的损失函数。


We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices. Our method learns to assign graph operations to groups and to allocate those groups to available devices. The grouping and device allocations are learned jointly. The proposed method is trained with policy gradient and requires no human intervention. Experiments with widely-used  computer vision and natural language models show that our algorithm can find optimized, non-trivial placements for TensorFlow computational graphs with over 80,000 operations. In addition, our approach outperforms placements by human  experts as well as a previous state-of-the-art placement method based on deep reinforcement learning. Our method achieves runtime reductions of up to 60.6% per training step when applied to models such as Neural Machine Translation.

我们引入了一个分层模型，用于将计算图高效地端对端放置在硬件设备上。

建议共同学习操作符的分组，将学习到的分组放在设备上，通过强化学习分配操作，进行深度学习。

作者的目的是用一个完全连接的网络来取代为加速TensorFlow模型的运行时间而提出的自动放置方法中的共同定位步骤。

提出了一种设备放置算法，将tensorflow的操作放在设备上。


Motion is an important signal for agents in dynamic environments, but learning to represent motion from unlabeled video is a difficult and underconstrained problem. We propose a model of motion based on elementary group properties of transformations and use it to train a representation of image motion. While most methods of estimating motion are based on pixel-level constraints, we use these group properties to constrain the abstract representation of motion itself. We demonstrate that a deep neural network trained using this method captures motion in both synthetic 2D sequences and real-world sequences of vehicle motion, without requiring any labels. Networks trained to respect these constraints implicitly identify the image characteristic of motion in different sequence types. In the context of vehicle motion, this method extracts information useful for localization, tracking, and odometry. Our results demonstrate that this representation is useful for learning motion in the general setting where explicit labels are difficult to obtain.

我们提出了利用群体属性来学习没有标签的运动表征的方法，并展示了这种方法在代表二维和三维运动方面的应用。

建议从图像序列的潜在表示中学习刚性运动组，而不需要明确的标签，并在MINST数字序列和KITTI数据集上实验证明了该方法。

本文提出了一种以无监督的方式学习视频运动特征的方法，使用约束条件来优化神经网络，以产生可用于回归里程的特征。


This paper introduces the concept of continuous convolution to neural networks and deep learning applications in general. Rather than directly using discretized information, input data is first projected into a high-dimensional Reproducing Kernel Hilbert Space (RKHS), where it can be modeled as a continuous function using a series of kernel bases. We then proceed to derive a closed-form solution to the continuous convolution operation between two arbitrary functions operating in different RKHS. Within this framework, convolutional filters also take the form of continuous functions, and the training procedure involves learning the RKHS to which each of these filters is projected, alongside their weight parameters. This results in much more expressive filters, that do not require spatial discretization and benefit from properties such as adaptive support and non-stationarity. Experiments on image classification are performed, using classical datasets, with results indicating that the proposed continuous convolutional neural network is able to achieve competitive accuracy rates with far fewer parameters and a faster convergence rate.

本文提出了一种新颖的卷积层，它在一个连续的再现核希尔伯特空间中运行。 将实例投射到RK希尔伯特空间，并对该空间进行卷积和过滤。 本文提出了一种卷积神经网络的变体，它将激活和过滤器建模为由核基组成的连续函数。


Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on 'Stylized-ImageNet', a stylized version of ImageNet. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation.

ImageNet训练的CNN偏向于物体纹理（而不是像人类一样偏向于形状）。克服人类和机器视觉之间的这一主要差异，可以产生更好的检测性能和以前没有见过的对图像失真的稳健性。

使用图像风格来增强ImageNet训练的CNN的训练数据，使产生的网络看起来与人类的判断更加一致


In this work, we exploited different strategies to provide prior knowledge to commonly used generative modeling approaches aiming to obtain speaker-dependent low dimensional representations from short-duration segments of speech data, making use of available information of speaker identities. Namely, convolutional variational autoencoders are employed, and statistics of its learned posterior distribution are used as low dimensional representations of fixed length short-duration utterances. In order to enforce speaker dependency in the latent layer, we introduced a variation of the commonly used prior within the variational autoencoders framework, i.e. the model is simultaneously trained for reconstruction of inputs along with a discriminative task performed on top of latent layers outputs. The effectiveness of both triplet loss minimization and speaker recognition are evaluated as implicit priors on the challenging cross-language NIST SRE 2016 setting and compared against fully supervised and unsupervised baselines.

我们评估了在由变分自动编码器学习的后验分布的统计资料上执行辅助判别任务的有效性，以执行说话人的依赖性。

提出一个自动编码器模型来学习使用短时分析窗口进行说话人验证的表示方法。

解决短时段说话人识别问题的变分自动编码器模型的修正版


The importance-weighted autoencoder (IWAE) approach of Burda et al. defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models. Recently, Cremer et al. reinterpreted the IWAE bounds as ordinary variational evidence lower bounds (ELBO) applied to increasingly accurate variational distributions. In this work, we provide yet another perspective on the IWAE bounds. We interpret each IWAE bound as a biased estimator of the true marginal likelihood where for the bound defined on $K$ samples we show the bias to be of order O(1/K). In our theoretical analysis of the IWAE objective we derive asymptotic bias and variance expressions. Based on this analysis we develop jackknife variational inference (JVI),  a family of bias-reduced estimators reducing the bias to $O(K^{-(m+1)})$ for any given m < K while retaining computational efficiency. Finally, we demonstrate that JVI leads to improved evidence estimates in variational autoencoders. We also report first results on applying JVI to learning variational autoencoders.   Our implementation is available at https://github.com/Microsoft/jackknife-variational-inference

变分推理是有偏差的，让我们对它去除偏差。

介绍了jackknife变分推理，这是一种对蒙特卡洛目标（如重要性加权的自动编码器）进行去重的方法。

作者分析了IWAE约束的偏差和方差，并推导出一种估计矩的jacknife方法，作为有限重要性加权样本的debias IWAE的方式。


In this paper, we consider the problem of autonomous lane changing for self driving vehicles in a multi-lane, multi-agent setting. We present a framework that demonstrates a more structured and data efficient alternative to end-to-end complete policy learning on problems where the high-level policy is hard to formulate using traditional optimization or rule based methods but well designed low-level controllers are available. Our framework uses deep reinforcement learning solely to obtain a high-level policy for tactical decision making, while still maintaining a tight integration with the low-level controller, thus getting the best of both worlds. We accomplish this with Q-masking, a technique with which we are able to incorporate prior knowledge, constraints, and information from a low-level controller, directly in to the learning process thereby simplifying the reward function and making learning faster and data efficient. We provide preliminary results in a simulator and show our approach to be more efficient than a greedy baseline, and more successful and safer than human driving.

一个框架，通过学习用深度强化学习做出高层次的战术决策，并与低层次的控制器保持紧密结合，采取低层次的行动，为自主改变车道提供政策。

考虑了在多车道多代理老虎车设置中自驾车的自主变道问题，提出了一种新的学习策略Q-masking--将确定的低级控制器与高级战术决策策略相耦合。

本文提出了一种使用 "Q-masking "的深度Q-learning方法来解决变道问题，该方法根据限制条件或先验知识来减少行动空间。

作者提出了一种方法，该方法使用基于Q-learning的高级策略，该策略与来自安全约束和低级控制器的上下文掩码相结合，使某些行动在某些状态下不能被选择。


Despite the recent successes in robotic locomotion control, the design of robot relies heavily on human engineering. Automatic robot design has been a long studied subject, but the recent progress has been slowed due to the large combinatorial search space and the difficulty in evaluating the found candidates. To address the two challenges, we formulate automatic robot design as a graph search problem and perform evolution search in graph space. We propose Neural Graph Evolution (NGE), which performs selection on current candidates and evolves new ones iteratively. Different from previous approaches, NGE uses graph neural networks to parameterize the control policies, which reduces evaluation cost on new candidates with the help of skill transfer from previously evaluated designs. In addition, NGE applies Graph Mutation with Uncertainty (GM-UC) by incorporating model uncertainty, which reduces the search space by balancing exploration and exploitation. We show that NGE significantly outperforms previous methods by an order of magnitude. As shown in experiments, NGE is the first algorithm that can automatically discover kinematically preferred robotic graph structures, such as a fish with two symmetrical flat side-fins and a tail, or a cheetah with athletic front and back legs. Instead of using thousands of cores for weeks, NGE efficiently solves searching problem within a day on a single 64 CPU-core Amazon EC2  machine.

用图神经网络自动搜索机器人设计

提出了一种基于神经图进化的自动机器人设计方法。实验证明，同时优化控制器和硬件要比只优化控制器要好。

作者提出了一个基于机器人结构的图表示的方案，以及作为控制器的图-神经-网络来优化机器人结构，并与他们的控制器相结合。


Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks only, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sidestep hurdles associated with linearization of such discrete structures by having a decoder output a probabilistic fully-connected graph of a predefined maximum size directly at once. Our method is formulated as a variational autoencoder. We evaluate on the challenging task of conditional molecule generation.

我们展示了一个图的自动编码器。

学习使用深度学习方法 "一次性 "地生成图，直接输出节点和边的存在概率，以及节点属性向量。

用于生成图形的变分自动编码器


Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling. Its goal is to use gates to control the information flow (e.g., whether to skip some information/transformation or not) in the recurrent computations, although its practical implementation based on soft gates only partially achieves this goal and is easy to overfit. In this paper, we propose a new way for LSTM training, which pushes the values of the gates towards 0 or 1. By doing so, we can (1) better control the information flow: the gates are mostly open or closed, instead of in a middle state; and (2) avoid overfitting to certain extent: the gates operate at their flat regions, which is shown to correspond to better generalization ability. However, learning towards discrete values of the gates is generally difficult. To tackle this challenge, we leverage the recently developed Gumbel-Softmax trick from the field of variational methods, and make the model trainable with standard backpropagation. Experimental results on language modeling and machine translation show that (1) the values of the gates generated by our method are more reasonable and intuitively interpretable, and (2) our proposed method generalizes better and achieves better accuracy on test sets in all tasks. Moreover, the learnt models are not sensitive to low-precision approximation and low-rank approximation of the gate parameters due to the flat loss surface.

我们提出了一种新的LSTM训练算法，通过向二值门学习，我们表明它具有许多良好的特性。

为LSTM提出一个新的 "门 "函数，使门的值朝向0或1。

本文旨在通过采用最近的Gumbel-Softmax技巧来获得端到端的可训练分类分布，从而推动LSTM门的二元化。


We present a personalized recommender system using neural network for recommending  products, such as eBooks, audio-books, Mobile Apps, Video and Music.  It produces recommendations based on customer’s implicit feedback history such  as purchases, listens or watches. Our key contribution is to formulate recommendation  problem as a model that encodes historical behavior to predict the future  behavior using soft data split, combining predictor and auto-encoder models. We  introduce convolutional layer for learning the importance (time decay) of the purchases  depending on their purchase date and demonstrate that the shape of the time  decay function can be well approximated by a parametrical function. We present  offline experimental results showing that neural networks with two hidden layers  can capture seasonality changes, and at the same time outperform other modeling  techniques, including our recommender in production. Most importantly, we  demonstrate that our model can be scaled to all digital categories, and we observe  significant improvements in an online A/B test. We also discuss key enhancements  to the neural network model and describe our production pipeline. Finally  we open-sourced our deep learning library which supports multi-gpu model parallel  training. This is an important feature in building neural network based recommenders  with large dimensionality of input and output data.

在一个零售网站的多个产品类别中使用神经网络的时间敏感模型来改进推荐。

本文提出了一种新的基于神经网络的推荐方法。

作者描述了从头开始建立他们的生产推荐系统的程序，并将购买的时间衰减纳入学习框架。


Deep Learning (DL) algorithms based on Generative Adversarial Network (GAN) have demonstrated great potentials in computer vision tasks such as image restoration. Despite the rapid development of image restoration algorithms using DL and GANs, image restoration for specific scenarios, such as medical image enhancement and super-resolved identity recognition, are still facing challenges. How to ensure visually realistic restoration while avoiding hallucination or mode- collapse? How to make sure the visually plausible results do not contain hallucinated features jeopardizing downstream tasks such as pathology identification and subject identification?  Here we propose to resolve these challenges by coupling the GAN based image restoration framework with another task-specific network. With medical imaging restoration as an example, the proposed model conducts additional pathology recognition/classification task to ensure the preservation of detailed structures that are important to this task. Validated on multiple medical datasets, we demonstrate the proposed method leads to improved deep learning based image restoration while preserving the detailed structure and diagnostic features. Additionally, the trained task network show potentials to achieve super-human level performance in identifying pathology and diagnosis.  Further validation on super-resolved identity recognition tasks also show that the proposed method can be generalized for diverse image restoration tasks.

将基于GAN的图像修复框架与另一个特定任务的网络结合起来，在保留特定任务特征的同时生成真实的图像。

一种新的图像耦合的任务--GAN方法，该方法将GAN和特定任务网络结合起来，缓解了避免幻觉或模式崩溃的情况。

作者建议用另一个特定任务的分支，如分类任务，来增强基于GAN的图像修复，以进一步改进。


Unsupervised anomaly detection on multi- or high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from decoupled model learning with inconsistent optimization goals and incapability of preserving essential information in the low-dimensional space. In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection. Our model utilizes a deep autoencoder to generate a low-dimensional representation and reconstruction error for each input data point, which is further fed into a Gaussian Mixture Model (GMM). Instead of using decoupled two-stage training and the standard Expectation-Maximization (EM) algorithm, DAGMM jointly optimizes the parameters of the deep autoencoder and the mixture model simultaneously in an end-to-end fashion, leveraging a separate estimation network to facilitate the parameter learning of the mixture model. The joint optimization, which well balances autoencoding reconstruction, density estimation of latent representation, and regularization, helps the autoencoder escape from less attractive local optima and further reduce reconstruction errors, avoiding the need of pre-training. Experimental results on several public benchmark datasets show that, DAGMM significantly outperforms state-of-the-art anomaly detection techniques, and achieves up to 14% improvement based on the standard F1 score.

一个端到端的训练有素的深度神经网络，利用高斯混合模型，在一个由深度自动编码器学习的低维空间中进行密度估计和无监督的异常检测。

本文提出了一个降维-聚类的联合深度学习框架，导致了有竞争力的异常检测。

一种新的异常检测技术，其中降维和密度估计步骤被联合优化。


Generalization from limited examples, usually studied under the umbrella of meta-learning, equips learning techniques with the ability to adapt quickly in dynamical environments and proves to be an essential aspect of lifelong learning. In this paper, we introduce the Projective Subspace Networks (PSN), a deep learning paradigm that learns non-linear embeddings from limited supervision. In contrast to previous studies, the embedding in PSN deems samples of a given class to form an affine subspace. We will show that such modeling leads to robust solutions, yielding competitive results on supervised and semi-supervised few-shot classification. Moreover, our PSN approach has the ability of end-to-end learning. In contrast to previous works, our projective subspace can be thought of as a richer representation capturing higher-order information datapoints for modeling new concepts.

我们提出了投射子空间网络，用于少样本和半监督的少样本学习

本文提出了一种新的基于嵌入的方法来解决少样本学习的问题，并将这种模式扩展到半监督的几率学习环境中。

基于学习一般嵌入，然后为每个类别学习其子空间的完全和半监督的少样本分类的新方法


This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning. To investigate this question, we consider an instantiation of this hypothesis evaluated on the Arcade Learning Element (ALE). In this study, we develop an attentive dynamics model (ADM) that discovers controllable elements of the observations, which are often associated with the location of the character in Atari games. The ADM is trained in a self-supervised fashion to predict the actions taken by the agent. The learned contingency information is used as a part of the state representation for exploration purposes. We demonstrate that combining actor-critic algorithm with count-based exploration using our representation achieves impressive results on a set of notoriously challenging Atari games due to sparse rewards. For example, we report a state-of-the-art score of >11,000 points on Montezuma's Revenge without using expert demonstrations, explicit high-level information (e.g., RAM states), or supervisory data. Our experiments confirm that contingency-awareness is indeed an extremely powerful concept for tackling exploration problems in reinforcement learning and opens up interesting research questions for further investigations.

我们研究了探索中的应急意识和可控制的方面，并在没有专家示范的情况下，在Montezuma's Revenge中取得了最先进的性能。

本文研究了在面临稀疏奖励任务时，通过识别状态的可控（学习）特征，提取有意义的状态表征来帮助探索的问题。

本文提出了在稀疏奖励强化学习任务中使用应急意识来帮助探索的新思路，获得了最先进的结果。


Disentangling factors of variation has always been a challenging problem in representation learning. Existing algorithms suffer from many limitations, such as unpredictable disentangling factors, bad quality of generated images from encodings, lack of identity information, etc. In this paper, we proposed a supervised algorithm called DNA-GAN trying to disentangle different attributes of images. The latent representations of images are DNA-like, in which each individual piece represents an independent factor of variation. By annihilating the recessive piece and swapping a certain piece of two latent representations, we obtain another two different representations which could be decoded into images. In order to obtain realistic images and also disentangled representations, we introduced the discriminator for adversarial training. Experiments on Multi-PIE and CelebA datasets demonstrate the effectiveness of our method and the advantage of overcoming limitations existing in other methods.

我们提出了一种有监督的算法，即DNA-GAN，来分解图像的多种属性。

本文研究了使用生成式对抗网络生成属性条件下的图像问题，并提出从属性和潜伏代码中生成图像作为高层表征。

本文提出了一种新的方法，使用新型DNA结构的GAN来分解图像的不同属性。


Representations learnt through deep neural networks tend to be highly informative, but opaque in terms of what information they learn to encode. We introduce an approach to probabilistic modelling that learns to represent data with two separate deep representations: an invariant representation that encodes the information of the class from which the data belongs, and an equivariant representation that encodes the symmetry transformation defining the particular data point within the class manifold (equivariant in the sense that the representation varies naturally with symmetry transformations). This approach to representation learning is conceptually transparent, easy to implement, and in-principle generally applicable to any data comprised of discrete classes of continuous distributions (e.g. objects in images, topics in language, individuals in behavioural data). We demonstrate qualitatively compelling representation learning and competitive quantitative performance, in both supervised and semi-supervised settings, versus comparable modelling approaches in the literature with little fine tuning.

本文提出了一种新型的潜变量生成模型技术，能够将全局信息表示为一个潜变量，将局部信息表示为另一个潜变量。

本文提出了一种VAE，它使用标签将学到的表征分成不变部分和协变部分。


Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe;  training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labelled from a  very large collection (i.e. active learning).   Our empirical study suggests that many of the active learning heuristics in the literature are not effective when applied to CNNs when applied in batch setting. Inspired by these limitations, we define the problem of active learning as core-set selection, i.e. choosing set of points such that a model learned over the selected subset is competitive for the remaining data points. We further present a theoretical result characterizing the performance of any selected subset using the geometry of the datapoints. As an active learning algorithm, we choose the subset which is expected to yield best result according to our characterization. Our experiments show that the proposed method significantly outperforms existing approaches in image classification experiments by a large margin.

我们把主动学习的问题看作是一个核心集的选择问题，并表明这种方法在批量主动学习的设置中特别有用，这在训练CNN时是至关重要的。

作者为多类分类提供了一种算法无关的主动学习算法

本文提出了一种针对CNN的批处理模式主动学习算法，作为一个核心集问题，其性能优于随机抽样和不确定性抽样。

研究卷积神经网络的主动学习，将主动学习问题表述为核心集选择，并提出了一种新的策略


Recurrent neural networks are known for their notorious exploding and vanishing gradient problem (EVGP). This problem becomes more evident in tasks where the information needed to correctly solve them exist over long time scales, because EVGP prevents important gradient components from being back-propagated adequately over a large number of steps. We introduce a simple stochastic algorithm (\textit{h}-detach) that is specific to LSTM optimization and targeted towards addressing this problem. Specifically, we show that when the LSTM weights are large, the gradient components through the linear path (cell state) in the LSTM computational graph get suppressed. Based on the hypothesis that these components carry information about long term dependencies (which we show empirically), their suppression can prevent LSTMs from capturing them. Our algorithm\footnote{Our code is available at https://github.com/bhargav104/h-detach. } prevents gradients flowing through this path from getting suppressed, thus allowing the LSTM to capture such dependencies better. We show significant improvements over vanilla LSTM gradient based training in terms of convergence speed, robustness to seed and learning rate, and generalization using our modification of LSTM gradient on various benchmark datasets.

改善LSTM中长期依赖性的优化和处理的简单算法

本文介绍了一种名为h-detach的简单随机算法，该算法专门针对LSTM优化，并以解决这一问题为目标。

提出了对LSTM训练过程的简单修改，以促进沿细胞状态的梯度传播，或 "线性时间路径"


Convolutional Neural Networks (CNNs) significantly improve the state-of-the-art for many applications, especially in computer vision. However, CNNs still suffer from a tendency to confidently classify out-distribution samples from unknown classes into pre-defined known classes. Further, they are also vulnerable to adversarial examples. We are relating these two issues through the tendency of CNNs to over-generalize for areas of the input space not covered well by the training set. We show that a CNN augmented with an extra output class can act as a simple yet effective end-to-end model for controlling over-generalization. As an appropriate training set for the extra class, we introduce two resources that are computationally efficient to obtain: a representative natural out-distribution set and interpolated in-distribution samples. To help select a representative natural out-distribution set among available ones, we propose a simple measurement to assess an out-distribution set's fitness. We also demonstrate that training such an augmented CNN with representative out-distribution natural datasets and some interpolated samples allows it to better handle a wide range of unseen out-distribution samples and black-box adversarial examples without training it on any adversaries. Finally, we show that generation of white-box adversarial attacks using our proposed augmented CNN can become harder, as the attack algorithms have to get around the rejection regions when generating actual adversaries.

用垃圾箱类适当地训练CNN，可以提高它们对对抗性攻击的稳健性，以及处理分布外样本的能力。

本文提出在CNN模型中增加一个额外的标签来检测OOD样本和对抗性例子。

本文提出了一个额外的类别，即在CNN中为对抗性样本和外分布样本纳入自然外分布图像和插值图像。


Modern deep artificial neural networks have achieved impressive results through models with very large capacity---compared to the number of training examples---that control overfitting with the help of different forms of regularization. Regularization can be implicit, as is the case of stochastic gradient descent or parameter sharing in convolutional layers, or explicit. Most common explicit regularization techniques, such as dropout and weight decay, reduce the effective capacity of the model and typically require the use of deeper and wider architectures to compensate for the reduced capacity. Although these techniques have been proven successful in terms of results, they seem to waste capacity. In contrast, data augmentation techniques reduce the generalization error by increasing the number of training examples and without reducing the effective capacity. In this paper we systematically analyze the effect of data augmentation on some popular architectures and conclude that data augmentation alone---without any other explicit regularization techniques---can achieve the same performance or higher as regularized models, especially when training with fewer examples.

在一个用足够的数据增强水平训练的深度卷积神经网络中，通过SGD优化，明确的正则器（权重衰减和放弃）可能不会提供任何额外的泛化改进。

本文提出了数据扩充作为常用正则化技术的替代方案，并表明对于少数参考模型/任务，仅使用数据扩充就能获得相同的泛化性能。

本文对深度神经网络在图像分类中的数据增强进行了系统研究，认为数据增强可以复制一些常见的正则器，如权重衰减和丢弃。


Text editing on mobile devices can be a tedious process. To perform various editing operations, a user must repeatedly move his or her fingers between the text input area and the keyboard, making multiple round trips and breaking the flow of typing. In this work, we present Gedit, a system of on-keyboard gestures for convenient mobile text editing. Our design includes a ring gesture and flicks for cursor control, bezel gestures for mode switching, and four gesture shortcuts for copy, paste, cut, and undo. Variations of our gestures exist for one and two hands. We conducted an experiment to compare Gedit with the de facto touch+widget based editing interactions. Our results showed that Gedit’s gestures were easy to learn, 24% and 17% faster than the de facto interactions for one- and two-handed use, respectively, and preferred by participants.

在这项工作中，我们提出了Gedit，一个用于方便移动文本编辑的键盘手势系统。 报告了Gedit交互技术的设计和评估。 提出了一套新的触摸手势，在移动设备中进行文本输入和文本编辑之间的无缝转换


Deep learning achieves remarkable generalization capability with overwhelming number of model parameters. Theoretical understanding of deep learning generalization receives recent attention yet remains not fully explored. This paper attempts to provide an alternative understanding from the perspective of maximum entropy. We first derive two feature conditions that softmax regression strictly apply maximum entropy principle. DNN is then regarded as approximating the feature conditions with multilayer feature learning, and proved to be a recursive solution towards maximum entropy principle. The connection between DNN and maximum entropy well explains why typical designs such as shortcut and regularization improves model generalization, and provides instructions for future model development.

我们证明，DNN是最大熵原理的递归近似解。 提出了一个推导，将DNN与最大熵模型拟合的递归应用联系起来。 本文旨在从最大熵原理的角度提供一个深度学习的观点。


As people learn to navigate the world, autonomic nervous system (e.g., ``fight or flight) responses provide intrinsic feedback about the potential consequence of action choices (e.g., becoming nervous when close to a cliff edge or driving fast around a bend.) Physiological changes are correlated with these biological preparations to protect one-self from danger. We present a novel approach to reinforcement learning that leverages a task-independent intrinsic reward function trained on peripheral pulse measurements that are correlated with human autonomic nervous system responses. Our hypothesis is that such reward functions can circumvent the challenges associated with sparse and skewed rewards in reinforcement learning settings and can help improve sample efficiency. We test this in a simulated driving environment and show that it can increase the speed of learning and reduce the number of collisions during the learning stage.

我们提出了一种新的强化学习方法，利用与任务无关的内在奖励函数，在与人类自律神经系统反应相关的周边脉搏测量上进行训练。

在自动驾驶的背景下，提出了一个基于人类情绪反应的强化学习框架。

作者提议在RL框架内使用信号，如影响决策的基本自律内脏反应，通过直接从人类神经系统反应中学习的模型来增强RL奖励功能。

提出利用生理信号来提高强化学习算法的性能，并通过测量心脏脉搏振幅来建立一个不那么稀疏的内在奖励函数。


Deep convolutional neural networks (CNNs) are known to be robust against label noise on extensive datasets. However, at the same time, CNNs are capable of memorizing all labels even if they are random, which means they can memorize corrupted labels. Are CNNs robust or fragile to label noise? Much of researches focusing on such memorization uses class-independent label noise to simulate label corruption, but this setting is simple and unrealistic. In this paper, we investigate the behavior of CNNs under class-dependently simulated label noise, which is generated based on the conceptual distance between classes of a large dataset (i.e., ImageNet-1k). Contrary to previous knowledge, we reveal CNNs are more robust to such class-dependent label noise than class-independent label noise. We also demonstrate the networks under class-dependent noise situations learn similar representation to the no noise situation, compared to class-independent noise situations.

CNN对标签噪声是稳健的还是脆弱的？实际上，稳健。

卷积神经网络模型在引入类相关和类独立噪声时的性能分析

证明了CNN对类相关的标签噪声具有更强的稳健性，并认为真实世界的噪声应该是类相关的。


Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence. Autoregressive models, such as WaveNet, model local structure at the expense of global latent structure and slow iterative sampling, while Generative Adversarial Networks (GANs), have global latent conditioning and efficient parallel sampling, but struggle to generate locally-coherent audio waveforms. Herein, we demonstrate that GANs can in fact generate high-fidelity and locally-coherent audio by modeling log magnitudes and instantaneous frequencies  with sufficient frequency resolution in the spectral domain. Through extensive empirical investigations on the NSynth dataset, we demonstrate that GANs are able to outperform strong WaveNet baselines on automated and human evaluation metrics, and efficiently generate audio several orders of magnitude faster than their autoregressive counterparts.

用GANs进行高质量的音频合成

提出了一种使用GAN框架的方法，通过在谱域中以足够的频率分辨率对对数幅度和瞬时频率进行建模来生成音频。

用GAN从噪声中生成音频样本的策略，对架构和表示方法进行必要的改变，以生成包含可解释的潜伏代码的令人信服的音频。

提出了一个简单的想法，以更好地代表音频数据，从而可以应用生成式对抗网络等卷积模型。


In this work we propose a novel approach for learning graph representation of the data using gradients obtained via backpropagation. Next we build a neural network architecture compatible with our optimization approach and motivated by graph filtering in the vertex domain. We demonstrate that the learned graph has richer structure than often used nearest neighbors graphs constructed based on features similarity. Our experiments demonstrate that we can improve prediction quality for several convolution on graphs architectures, while others appeared to be insensitive to the input graph.

顶点域中的信号过滤的图形优化。

本文研究了使用投影子梯度下降算法学习具有非负边缘权重的稀疏连接无向图的邻接矩阵。

开发了一种新的神经网络图的邻接矩阵的反推方案


The use of AR in an industrial context could help for the training of new operators. To be able to use an AR guidance system, we need a tool to quickly create a 3D representation of the assembly line and of its AR annotations. This tool should be very easy to use by an operator who is not an AR or VR specialist: typically the manager of the assembly line. This is why we proposed WAAT, a 3D authoring tool allowing user to quickly create 3D models of the workstations, and also test the AR guidance placement. WAAT makes on-site authoring possible, which should really help to have an accurate 3D representation of the assembly line. The verification of AR guidance should also be very useful to make sure everything is visible and doesn't interfere with technical tasks. In addition to these features, our future work will be directed in the deployment of WAAT into a real boiler assembly line to assess the usability of this solution.

本文描述了一种用于在工业4.0的装配线上提供AR的3D创作工具。 本文讨论了AR创作工具如何支持装配线系统的培训，并提出了一种方法 一个用于工业装配线的AR指导系统，可以现场编写AR内容。 提出了一个系统，允许工厂工人使用增强现实系统进行更有效的培训。


Generative adversarial network (GAN) is one of the best known unsupervised learning techniques these days due to its superior ability to learn data distributions. In spite of its great success in applications, GAN is known to be notoriously hard to train. The tremendous amount of time it takes to run the training algorithm and its sensitivity to hyper-parameter tuning have been haunting researchers in this area. To resolve these issues, we need to first understand how GANs work. Herein, we take a step toward this direction by examining the dynamics of GANs. We relate a large class of GANs including the Wasserstein GANs to max-min optimization problems with the coupling term being linear over the discriminator. By developing new primal-dual optimization tools, we show that, with a proper stepsize choice, the widely used first-order iterative algorithm in training GANs would in fact converge to a stationary solution with a sublinear rate. The same framework also applies to multi-task learning and distributional robust learning problems. We verify our analysis on numerical examples with both synthetic and real data sets. We hope our analysis shed light on future studies on the theoretical properties of relevant machine learning problems.

我们表明，如果选择适当的步长，在训练GAN中广泛使用的一阶迭代算法实际上会以亚线性的速度收敛到静止的解决方案。

本文使用GAN和多任务学习为某些最小最大值问题上的原始-双重算法提供了收敛保证。

通过假设一类有限的模型，将问题表述为原始-双重优化问题，分析了GAN的学习动力学。


Social dilemmas, where mutual cooperation can lead to high payoffs but participants face incentives to cheat, are ubiquitous in multi-agent interaction. We wish to construct agents that cooperate with pure cooperators, avoid exploitation by pure defectors, and incentivize cooperation from the rest. However, often the actions taken by a partner are (partially) unobserved or the consequences of individual actions are hard to predict. We show that in a large class of games good strategies can be constructed by conditioning one's behavior solely on outcomes (ie. one's past rewards). We call this consequentialist conditional cooperation. We show how to construct such strategies using deep reinforcement learning techniques and demonstrate, both analytically and experimentally, that they are effective in social dilemmas beyond simple matrix games. We also show the limitations of relying purely on consequences and discuss the need for understanding both the consequences of and the intentions behind an action.

我们展示了如何使用深度RL来构建能够解决矩阵游戏之外的社会困境的代理。

学习玩有不完全信息的状态的双人一般和的游戏

规定了一个触发策略（CCC）和相应的算法，证明了在社会困境中收敛到有效的结果，而不需要代理观察对方的行动。


In distributed training, the communication cost due to the transmission of gradients  or the parameters of the deep model is a major bottleneck in scaling up the number  of processing nodes. To address this issue, we propose dithered quantization for  the transmission of the stochastic gradients and show that training with Dithered  Quantized Stochastic Gradients (DQSG) is similar to the training with unquantized  SGs perturbed by an independent bounded uniform noise, in contrast to the other  quantization methods where the perturbation depends on the gradients and hence,  complicating the convergence analysis. We study the convergence of training  algorithms using DQSG and the trade off between the number of quantization  levels and the training time. Next, we observe that there is a correlation among the  SGs computed by workers that can be utilized to further reduce the communication  overhead without any performance loss. Hence, we develop a simple yet effective  quantization scheme, nested dithered quantized SG (NDQSG), that can reduce the  communication significantly without requiring the workers communicating extra  information to each other. We prove that although NDQSG requires significantly  less bits, it can achieve the same quantization variance bound as DQSG. Our  simulation results confirm the effectiveness of training using DQSG and NDQSG  in reducing the communication bits or the convergence time compared to the  existing methods without sacrificing the accuracy of the trained model.

本文提出并分析了在分布式学习中交流随机梯度的两种量化方案，与目前的技术水平相比，这将减少通信成本，同时保持相同的精度。

作者提出将抖动量化应用于通过训练过程计算的随机梯度，这改善了量化误差，与基线相比取得了卓越的结果，并提出了一个嵌套方案以减少通信成本。

作者建立了分布式优化中的通信减少和抖动量化之间的联系，并开发了两种新的分布式训练算法，其中通信开销被大大减少。


Deep neural networks have been shown to perform well in many classical machine learning problems, especially in image classification tasks. However, researchers have found that neural networks can be easily fooled, and they are surprisingly sensitive to small perturbations imperceptible to humans.   Carefully crafted input images (adversarial examples) can force a well-trained neural network to provide arbitrary outputs.   Including adversarial examples during training is a popular defense mechanism against adversarial attacks. In this paper we propose a new defensive mechanism under the generative adversarial network~(GAN) framework. We model the adversarial noise using a generative network, trained jointly with a classification discriminative network as a minimax game. We show empirically that our adversarial network approach works well against black box attacks, with performance on par with state-of-art methods such as ensemble adversarial training and adversarial training with projected gradient descent.

将对抗性噪声生成网络与分类网络联合训练，对对抗性攻击提供更好的稳健性。

一个用于深度分类模型的GAN解决方案，面对白盒和黑盒攻击，能生成稳健的模型。

本文提出了一种防御机制，利用GAN的生成扰动作为对抗性例子，并使用判别器来区分对抗性攻击。


Deep learning has become the state of the art approach in many machine learning problems such as classification. It has recently been shown that deep learning is highly vulnerable to adversarial perturbations. Taking the camera systems of self-driving cars as an example, small adversarial perturbations can cause the system to  make errors in important tasks, such as classifying traffic signs or detecting pedestrians. Hence, in order to use deep learning without safety concerns a proper defense strategy is required. We propose to use ensemble methods as a defense strategy against adversarial perturbations. We find that an attack leading one model to misclassify does not imply the same for other networks performing the same task. This makes ensemble methods an attractive defense strategy against adversarial attacks. We empirically show for the MNIST and the CIFAR-10 data sets that ensemble methods not only improve the accuracy of neural networks on test data but also increase their robustness against adversarial perturbations.

使用集成方法作为对深度神经网络的对抗性扰动的防御。

本文提出将集成作为一种对抗性防御机制。

在MNIST和CIFAR10这两个流行的数据集上，实证研究了不同的深度神经工程组合对FGSM和BIM这两类攻击的稳健性。


In this paper, we propose the Associative Conversation Model that generates visual information from textual information and uses it for generating sentences in order to utilize visual information in a dialogue system without image input. In research on Neural Machine Translation, there are studies that generate translated sentences using both images and sentences, and these studies show that visual information improves translation performance. However, it is not possible to use sentence generation algorithms using images for the dialogue systems since many text-based dialogue systems only accept text input. Our approach generates (associates) visual information from input text and generates response text using context vector  fusing associative visual information and sentence textual information. A comparative experiment between our proposed model and a model without association showed that our proposed model is generating useful sentences by associating visual information related to sentences. Furthermore, analysis experiment of visual association showed that our proposed model generates (associates) visual information effective for sentence generation.

基于文本信息和与文本信息相关的视觉信息融合的句子生成方法的建议

这项工作描述了一个用于对话系统的深度学习模型，它利用了视觉信息。

本文提出了一个新的基础对话的数据集，并提出了一个计算上的观察，即即使在进行基于文本的对话时，它也可以帮助推理视觉。

建议通过收集由文本和相关图像或视频组成的一堆数据，将视觉信息纳入其中，从而增强传统的基于文本的句子生成/对话方法。


Feedforward convolutional neural network has achieved a great success in many computer vision tasks. While it validly imitates the hierarchical structure of biological visual system, it still lacks one essential architectural feature: contextual recurrent connections with feedback, which widely exists in biological visual system. In this work, we designed a Contextual Recurrent Convolutional Network with this feature embedded in a standard CNN structure. We found that such feedback connections could enable lower layers to ``rethink" about their representations given the top-down contextual information. We carefully studied the components of this network, and showed its robustness and superiority over feedforward baselines in such tasks as noise image classification, partially occluded object recognition and fine-grained image classification. We believed this work could be an important step to help bridge the gap between computer vision models and real biological visual system.

我们提出了一种新型的情景循环卷积网络，具有视觉学习的鲁棒性。
本文介绍了反馈连接，通过纳入上下文信息来加强特征学习。
本文提出在卷积网络中加入 "循环"连接，并采用门控机制。


Deep neural networks have led to a series of breakthroughs, dramatically improving the state-of-the-art in many domains. The techniques driving these advances, however, lack a formal method to account for model uncertainty. While the Bayesian approach to learning provides a solid theoretical framework to handle uncertainty, inference in Bayesian-inspired deep neural networks is difficult. In this paper, we provide a practical approach to Bayesian learning that relies on a regularization technique found in nearly every modern network, batch normalization. We show that training a deep network using batch normalization is equivalent to approximate inference in Bayesian models, and we demonstrate how this finding allows us to make useful estimates of the model uncertainty. Using our approach, it is possible to make meaningful uncertainty estimates using conventional architectures without modifying the network or the training procedure. Our approach is thoroughly validated in a series of empirical experiments on different tasks and using various measures, showing it to outperform baselines on a majority of datasets with strong statistical significance.

我们表明，使用批量归一化训练深度网络等同于贝叶斯模型的近似推理，我们还证明了这一发现如何让我们对传统网络的模型不确定性做出有用的估计。

本文提出在测试时使用批量规范化来获得预测的不确定性，并表明在测试时使用批量规范的蒙特卡洛预测优于丢弃。

提出被称为批量规范化的规范化程序可以被理解为进行近似的贝叶斯推理，就其产生的不确定性估计值而言，其表现与MC dropout相似。


Data-parallel neural network training is network-intensive, so gradient dropping was designed to exchange only large gradients.   However, gradient dropping has been shown to slow convergence.   We propose to improve convergence by having each node combine its locally computed gradient with the sparse global gradient exchanged over the network. We empirically confirm with machine translation tasks that gradient dropping with local gradients approaches convergence 48% faster than non-compressed multi-node training and 28% faster compared to vanilla gradient dropping. We also show that gradient dropping with a local gradient update does not reduce the model's final quality.

我们改进了梯度下降（一种在分布式训练中只交换大梯度的技术），在做参数更新时加入了局部梯度，以减少质量损失并进一步提高训练时间。

本文提出了一种结合局部和全局梯度的3种模式，以更好地利用更多的计算节点

研究减少实施分布式优化技术的通信要求问题，特别是SGD。


We establish the relation between Distributional RL and the Upper Confidence Bound (UCB) approach to exploration.      In this paper we show that the density of the Q function estimated by Distributional RL can be successfully used for the estimation of UCB. This approach does not require counting and, therefore, generalizes well to the Deep RL. We also point to the asymmetry of the empirical densities estimated by the Distributional RL algorithms like QR-DQN. This observation leads to the reexamination of the variance's performance in the UCB type approach to exploration. We introduce truncated variance as an alternative estimator of the UCB and a novel algorithm based on it. We empirically show that newly introduced algorithm achieves better performance in multi-armed bandits setting. Finally, we extend this approach to high-dimensional setting and test it on the Atari 2600 games. New approach achieves better performance compared to QR-DQN in 26 of games, 13 ties out of 49 games.

使用分布式RL和截断方差进行的探索。

提出了一种通过UCB技术管理探索-开发权衡的RL方法。

一种使用分位数回归DQN学到的分布进行探索的方法，以取代通常的ε-greedy策略。

提出了新的算法（QUCB和QUCB+）来处理Multi-Armed Bandit和更普遍的强化学习中的探索权衡。


Good representations facilitate transfer learning and few-shot learning. Motivated by theories of language and communication that explain why communities with large number of speakers have, on average, simpler languages with more regularity, we cast the representation learning problem in terms of learning to communicate. Our starting  point sees traditional autoencoders as  a single encoder with a fixed decoder partner that must learn to communicate. Generalizing from there, we introduce community-based autoencoders in which multiple encoders and decoders collectively learn representations by being randomly paired up on successive training iterations. Our experiments show that increasing community sizes reduce idiosyncrasies in the learned codes, resulting in more invariant representations with increased reusability and structure.

在语言和通信理论的推动下，我们引入了基于社区的自动编码器，其中多个编码器和解码器共同学习结构化和可重复使用的表征。

作者解决了表征学习的问题，旨在建立可重复使用和结构化的表征，认为传统AE中编码器和解码器之间的共同适应会产生糟糕的表征，并介绍了基于社区的自动编码器。

本文提出了一个基于社区的自动编码器框架，以解决编码器和解码器的共同适应问题，旨在构建更好的表征。


Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a  task instance, and to bootstrap learning of new tasks. Achieving these abilities in autonomous agents is an open problem. In this paper, we introduce an off-policy RL algorithm (MetaMimic) to narrow this gap. MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators. MetaMimic relies on the principle of storing all experiences in a memory and replaying these to learn massive deep neural network policies by off-policy RL. This paper introduces, to the best of our knowledge, the largest existing neural networks for deep RL and shows that larger networks with normalization are needed to achieve one-shot high-fidelity imitation on a challenging manipulation task.  The results also show that both types of policy can be learned from vision, in spite of the task rewards being sparse, and without access to demonstrator actions.

我们提出了MetaMimic算法，该算法将示范数据集作为输入，并输出（i）一次性高保真模仿策略和（ii）无条件的任务策略。

本文研究了具有高模仿精度的一次性模仿问题，将DDPGfD扩展到只使用状态轨迹。

本文提出了一种具有高精确度的一次性模仿方法，并解决了模仿学习中常见的探索问题。

提出了一种从视频演示中学习的RL方法，不需要获得专家的行动。


Normalization methods are a central building block in the deep learning toolbox. They accelerate and stabilize training, while decreasing the dependence on manually tuned learning rate schedules. When learning from multi-modal distributions, the effectiveness of batch normalization (BN), arguably the most prominent normalization method, is reduced. As a remedy, we propose a more flexible approach: by extending the normalization to more than a single mean and variance, we detect modes of data on-the-fly, jointly normalizing samples that share common features. We demonstrate that our method outperforms BN and other widely used normalization techniques in several experiments, including single and multi-task datasets.

我们提出了一种新的深度神经网络的归一化方法，对中间特征分布中的多模态具有稳健性。

在特征空间中学习多模态分布的归一化方法

在单元激活在批处理和空间维度上的统计数字不是单峰的假设下，提出了批处理归一化的一般化。


Multilingual machine translation, which translates multiple languages with a single model, has attracted much attention due to its efficiency of offline training and online serving. However, traditional multilingual translation usually yields inferior accuracy compared with the counterpart using individual models for each language pair, due to language diversity and model capacity limitations. In this paper, we propose a distillation-based approach to boost the accuracy of multilingual machine translation. Specifically, individual models are first trained and regarded as teachers, and then the multilingual model is trained to fit the training data and match the outputs of individual models simultaneously through knowledge distillation. Experiments on IWSLT, WMT and Ted talk translation datasets demonstrate the effectiveness of our method. Particularly, we show that one model is enough to handle multiple languages (up to 44 languages in our experiment), with comparable or even better accuracy than individual models.

我们提出了一种基于知识提炼的方法来提高多语言神经机器翻译的准确性。

一个多对一的多语言神经机器翻译模型，首先为每个语言对训练单独的模型，然后进行蒸馏。

本文旨在通过用基于单个（单语言对）教师模型的提炼成分来增强标准的交叉熵损失，来训练机器翻译模型。


What makes humans so good at solving seemingly complex video games?   Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation studies to quantify the importance of various priors. We do this by modifying the video game environment to systematically mask different types of visual information that could be used by humans as priors. We find that removal of some prior knowledge causes a drastic degradation in the speed with which human players solve the game, e.g. from 2 minutes to over 20 minutes. Furthermore, our results indicate that general priors, such as the importance of objects and visual consistency, are critical for efficient game-play.

我们研究了有助于人类学习的各种先验知识，发现关于物体的一般先验知识在指导人类游戏方面发挥了最关键的作用。

作者通过实验研究了人类先验的哪些方面对视频游戏中的强化学习是重要的。

作者提出了一项关于人类在玩视频游戏时采用的先验因素的研究，并证明了存在一种分类法，这些特征在不同程度上影响着完成游戏中的任务的能力。


Driven by the need for parallelizable hyperparameter optimization methods, this paper studies \emph{open loop} search methods: sequences that are predetermined and can be generated before a single configuration is evaluated. Examples include grid search, uniform random search, low discrepancy sequences, and other sampling distributions.  In particular, we propose the use of $k$-determinantal point processes in  hyperparameter optimization via random search. Compared to conventional uniform random search where hyperparameter settings are sampled independently, a $k$-DPP promotes diversity.   We describe an approach that transforms hyperparameter search spaces for efficient use with a $k$-DPP. In addition, we introduce a novel Metropolis-Hastings algorithm which can sample from $k$-DPPs defined over spaces with a mixture of discrete and continuous dimensions. Our experiments show significant benefits over uniform random search  in realistic scenarios with a limited budget for training supervised learners, whether in serial or parallel.

在对可并行的、开环的超参数优化方法的需求驱动下，我们提出在通过随机搜索进行的超参数优化中使用k行列式的点过程。

提议在超参数搜索中使用k-DPP来选择候选点。

作者提出了k-DPP作为超参数优化的开环方法，并提供了其实证研究和与其他方法的比较。

考虑使用行列式的点过程进行非顺序的和不知情的超参数搜索，这是在地面集的子集上的概率分布，其特性是具有更多 "不同 "元素的子集有更高的概率。


In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch.  When using fine-tuning, the underlying assumption is that the pre-trained model extracts generic features, which are at least partially relevant for solving the target task, but would be difficult to extract from the limited amount of data available on the target task.  However, besides the initialization with the pre-trained model and the early stopping, there is no mechanism in fine-tuning for retaining the features learned on the source task.  In this paper, we investigate several regularization schemes that explicitly promote the similarity of the final solution with the initial model.  We eventually recommend a simple $L^2$ penalty using the pre-trained model as a reference, and we show that this approach behaves much better than the standard scheme using weight decay on a partially frozen network.

在归纳迁移学习中，对预先训练好的卷积网络进行微调，大大超过了从头训练的效果。 解决了深度网络中的转移学习问题，并提出要有一个正则化项来惩罚与初始化的分歧。 提出了对深度迁移学习的不同自适应正则化技术的分析，特别关注L@-SP条件的使用


Artificial neural networks have opened up a world of possibilities in data science and artificial intelligence, but neural networks are cumbersome tools that grow with the complexity of the learning problem. We make contributions to this issue by considering a modified version of the fully connected layer we call a block diagonal inner product layer. These modified layers have weight matrices that are block diagonal, turning a single fully connected layer into a set of densely connected neuron groups. This idea is a natural extension of group, or depthwise separable, convolutional layers applied to the fully connected layers. Block diagonal inner product layers can be achieved by either initializing a purely block diagonal weight matrix or by iteratively pruning off diagonal block entries. This method condenses network storage and speeds up the run time without significant adverse effect on the testing accuracy, thus offering a new approach to improve network computation efficiency.

我们着眼于具有块状对角线内积层的神经网络，以提高效率。

本文提出使神经网络中的内层为块状对角线，并讨论了块状对角线矩阵比修剪更有效，块状对角线层导致更有效的网络。

用块状对角线全连接层取代全连接层


One of the challenges in the study of generative adversarial networks is the instability of its training.   In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator.  Our new normalization technique is computationally light and easy to incorporate into existing implementations.   We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.

我们提出了一种新的权重归一化技术，即光谱归一化，以稳定GAN的判别器的训练。

本文使用光谱正则化来规范GAN目标，随之而来的GAN被称为SN-GAN，基本上保证了判别器的Lipschitz属性。

本文提出了 "光谱归一化"，在改善GAN的训练方面向前迈出了一大步。


Humans acquire complex skills by exploiting previously learned skills and making transitions between them. To empower machines with this ability, we propose a method that can learn transition policies which effectively connect primitive skills to perform sequential tasks without handcrafted rewards. To efficiently train our transition policies, we introduce proximity predictors which induce rewards gauging proximity to suitable initial states for the next skill. The proposed method is evaluated on a set of complex continuous control tasks in bipedal locomotion and robotic arm manipulation which traditional policy gradient methods struggle at. We demonstrate that transition policies enable us to effectively compose complex skills with existing primitive skills. The proposed induced rewards computed using the proximity predictor further improve training efficiency by providing more dense information than the sparse rewards from the environments. We make our environments, primitive skills, and code public for further research at https://youngwoon.github.io/transition .

过渡策略使代理人能够通过平稳地连接以前获得的原始技能来组成复杂的技能。

提出了一种在连续域中执行给定选项时过渡到有利的分层状态的方案。这使用了两个同时进行的学习过程。

提出了一种学习从一个任务过渡到另一个任务的策略的方法，目的是利用状态接近估计器对过渡策略进行奖励，完成复杂的任务。

提出了一个新的训练方案，用学习到的辅助奖励函数来优化过渡策略，将前一个宏操作/选择的结束状态与后一个宏操作/选择的良好启动状态连接起来。


Gated recurrent units (GRUs) were inspired by the common gated recurrent unit, long short-term memory (LSTM), as a means of capturing temporal structure with less complex memory unit architecture. Despite their incredible success in tasks such as natural and artificial language processing, speech, video, and polyphonic music, very little is understood about the specific dynamic features representable in a GRU network. As a result, it is difficult to know a priori how successful a GRU-RNN will perform on a given data set. In this paper, we develop a new theoretical framework to analyze one and two dimensional GRUs as a continuous dynamical system, and classify the dynamical features obtainable with such system.  We found rich repertoire that includes stable limit cycles over time (nonlinear oscillations), multi-stable state transitions with various topologies, and homoclinic orbits. In addition, we show that any finite dimensional GRU cannot precisely replicate the dynamics of a ring attractor, or more generally, any continuous attractor, and is limited to finitely many isolated fixed points in theory. These findings were then experimentally verified in two dimensions by means of time series prediction.

我们对一个和两个GRU单元能够和不能在连续时间内捕获的动态特征进行了分类，并通过k-step时间序列预测实验验证了我们的发现。

作者将隐藏规模为1和2的GRU分析为连续时间动态系统，声称隐藏状态表示的表达能力可以提供关于GRU在给定数据集上表现如何的先验知识。

本文从动态系统的角度分析了GRU，并表明2d GRU可以被训练成采用各种固定点，并可以近似于线吸引子，但不能模仿环形吸引子。

将GRU方程转换为连续时间，利用理论和经验研究一维和二维GRU网络，并展示这些系统中的各种动态拓扑结构。


Stacked hourglass network has become an important model for Human pose estimation. The estimation of human body posture depends on the global information of the keypoints type and the local information of the keypoints location. The consistent processing of inputs and constraints makes it difficult to form differentiated and determined collaboration mechanisms for each stacked hourglass network. In this paper, we propose a Multi-Scale Stacked Hourglass (MSSH) network to high-light the differentiation capabilities of each Hourglass network for human pose estimation.   The pre-processing network forms feature maps of different scales,and dispatch them to various locations of the stack hourglass network, where the small-scale features reach the front of stacked hourglass network, and large-scale features reach the rear of stacked hourglass network.    And a new loss function is proposed for multi-scale stacked hourglass network.   Different keypoints have different weight coefficients of loss function at different scales, and the keypoints weight coefficients are dynamically adjusted from the top-level hourglass network to the bottom-level hourglass network.   Experimental results show that the pro-posed method is competitive with respect to the comparison algorithm on MPII and LSP datasets.

差异化的输入会引起网络的功能差异化，网络之间损失函数的相互作用会影响优化过程。

对原始沙漏网络的修改，用于单一姿势的估计，产生了比原始基线的改进。

作者用inception-resnet-A模块扩展了一个堆叠的沙漏网络，并提出了一种多尺度的方法，用于静态RGB图像中的人体姿势估计。


We present a new unsupervised method for learning general-purpose sentence embeddings.  Unlike existing methods which rely on local contexts, such as words  inside the sentence or immediately neighboring sentences, our method selects, for  each target sentence, influential sentences in the entire document based on a document  structure. We identify a dependency structure of sentences using metadata  or text styles. Furthermore, we propose a novel out-of-vocabulary word handling  technique to model many domain-specific terms, which were mostly discarded by  existing sentence embedding methods. We validate our model on several tasks  showing 30% precision improvement in coreference resolution in a technical domain,  and 7.5% accuracy increase in paraphrase detection compared to baselines.

为了使用技术文件训练句子嵌入，我们的方法考虑了文件结构，以寻找更广泛的上下文并处理词汇外的单词。

提出了通过借鉴更多的语境来改善句子嵌入的想法。

用句子的依赖性信息学习句子表征

通过使用更广泛的证据集来形成句子的表征，扩展了SkipThough方法中使用的形成无监督的句子表征的想法


Neural network training relies on our ability to find ````````"good" minimizers of highly non-convex loss functions. It is well known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and well-chosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effect on the underlying loss landscape, is not well understood.   In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple ``"filter normalization" method that helps us visualize loss function curvature, and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture effects the loss landscape, and how training parameters affect the shape of minimizers.

我们使用一系列的可视化方法探索神经损失函数的结构，以及损失景观对泛化的影响。

本文提出了一种将NN的损失函数可视化的方法，并对NN的可训练性和泛化提出了见解。

研究了损失面和优化路径的非凸性。


Deep models are state-of-the-art for many computer vision tasks including image classification and object detection. However, it has been shown that deep models are vulnerable to adversarial examples. We highlight how one-hot encoding directly contributes to this vulnerability and propose breaking away from this widely-used, but highly-vulnerable mapping. We demonstrate that by leveraging a different output encoding, multi-way encoding, we can make models more robust. Our approach makes it more difficult for adversaries to find useful gradients for generating adversarial attacks. We present state-of-the-art robustness results for black-box, white-box attacks, and achieve higher clean accuracy on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN when combined with adversarial training. The strength of our approach is also presented in the form of an attack for model watermarking, raising challenges in detecting stolen models.

我们证明，通过利用多向输出编码，而不是广泛使用的独热编码，我们可以使深度模型对对抗性攻击更加稳健。

本文提出通过将每个标签编码为高维向量，并训练分类器以最小化与正确类别的编码的L2距离，来取代分类器中以一热标签训练的最终交叉熵层。

作者提出了对抗对抗性攻击的新方法，与基线相比，显示出显著的收益。


Existing approaches to neural machine translation condition each output word on previously generated outputs. We introduce a model that avoids this autoregressive property and produces its outputs in parallel, allowing an order of magnitude lower latency during inference. Through knowledge distillation, the use of input token fertilities as a latent variable, and policy gradient fine-tuning, we achieve this at a cost of as little as 2.0 BLEU points relative to the autoregressive Transformer network used as a teacher. We demonstrate substantial cumulative improvements associated with each of the three aspects of our training strategy, and validate our approach on IWSLT 2016 English–German and two WMT language pairs. By sampling fertilities in parallel at inference time, our non-autoregressive model achieves near-state-of-the-art performance of 29.8 BLEU on WMT 2016 English–Romanian.

我们引入了第一个具有完全并行解码的NMT模型，将推理延迟降低了10倍。

这项工作为编码器-解码器框架提出了非自回归解码器，其中生成单词的决定不取决于已生成的词的先前决定。

本文介绍了一种对神经机器翻译进行非自回归解码的方法，有可能实现更多的并行解码，这可以使速度大大加快。

建议引入一组潜变量来代表每个源词的生育率，使目标句子的生成非自回归。


While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks that defeat these defenses. Can we somehow end this arms race? In this work, we study this problem for neural networks with one hidden layer. We first propose a method based on a semidefinite relaxation that outputs a certificate that for a given network and test input, no attack can force the error to exceed a certain value. Second, as this certificate is differentiable, we jointly optimize it with the network parameters, providing an adaptive regularizer that encourages robustness against all attacks. On MNIST, our approach produces a network and a certificate that no that perturbs each pixel by at most $\epsilon = 0.1$ can cause more than $35\%$ test error.

我们展示了一种可认证的、可训练的和可扩展的方法，用于防御对抗性实例。 提出了一种新的防御神经网络安全攻击的方法，其攻击模型可以输出算法的安全证书。 推导出对具有一个隐藏层的神经网络的对抗性扰动的上限


We formulate an information-based optimization problem for supervised classification. For invertible neural networks, the control of these information terms is passed down to the latent features and parameter matrix in the last fully connected layer, given that mutual information is invariant under invertible map.   We propose an objective function and prove that it solves the optimization problem. Our framework allows us to learn latent features in an more interpretable form while improving the classification performance. We perform extensive quantitative and qualitative experiments in comparison with the existing state-of-the-art classification models.

我们提出了一种能提高神经网络分类性能的正则器

作者建议从预测和真实输出之间的相互信息最大化的角度来训练模型，并在学习的同时将不相关的信息降到最低的正则化项。

提出将参数分解为可逆的特征图F和最后一层的线性变换w，以使相互信息I(Y, hat{T})最大化，同时约束不相关信息


Powerful generative models, particularly in Natural Language Modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood. These models often suffer from poor use of their latent variable, with ad-hoc annealing factors used to encourage retention of information in the latent variable. We discuss an alternative and general approach to latent variable modelling, based on an objective that encourages a perfect reconstruction by tying a stochastic autoencoder with a variational autoencoder (VAE). This ensures by design that the latent variable captures information about the observations, whilst retaining the ability to generate well. Interestingly, although our model is fundamentally different to a VAE, the lower bound attained is identical to the standard VAE bound but with the addition of a simple pre-factor; thus, providing a formal interpretation of the commonly used, ad-hoc pre-factors in training VAEs.

本文介绍了一个新的生成模型框架，该框架避免了潜在变量的崩溃，并阐明了在训练变分自动编码器时使用某些特殊的因素。

本文提出解决关于变分自动编码器忽略潜在变量的问题。

本文提出在原VAE模型中加入随机自动编码器，以解决语言模型的LSTM解码器可能过于强大而忽略潜变量信息的问题。

本文介绍了AutoGen，它将生成式变分自动编码器与基于自动编码器的高保真重建模型结合起来，以更好地利用潜在的表示方法。


This paper studies the problem of domain division which aims to segment instances drawn from different probabilistic distributions. This problem exists in many previous recognition tasks, such as Open Set Learning (OSL) and Generalized Zero-Shot Learning (G-ZSL), where the testing instances come from either seen or unseen/novel classes with different probabilistic distributions. Previous works only calibrate the conﬁdent prediction of classiﬁers of seen classes (WSVM Scheirer et al. (2014)) or taking unseen classes as outliers Socher et al. (2013). In contrast, this paper proposes a probabilistic way of directly estimating and ﬁne-tuning the decision boundary between seen and unseen classes. In particular, we propose a domain division algorithm to split the testing instances into known, unknown and uncertain domains, and then conduct recognition tasks in each domain. Two statistical tools, namely, bootstrapping and KolmogorovSmirnov (K-S) Test, for the ﬁrst time, are introduced to uncover and ﬁne-tune the decision boundary of each domain. Critically, the uncertain domain is newly introduced in our framework to adopt those instances whose domain labels cannot be predicted conﬁdently. Extensive experiments demonstrate that our approach achieved the state-of-the-art performance on OSL and G-ZSL benchmarks.

本文通过分割来自不同概率分布的实例来研究领域划分的问题。

本文论述了开放集学习和广义零样本学习中的新颖性识别问题，并提出了一种可能的解决方案

一种基于引导的领域分离方法，以确定已知类别的相似性截止阈值，然后用Kolmogorov-Smirnoff测试来完善引导的内分布区。

建议引入一个新的域，即不确定域，以更好地处理开放集和广义零样本学习中看到/看不到的域的划分。


Stochastic gradient descent (SGD) is widely believed to perform implicit regularization when used to train deep neural networks, but the precise manner in which this occurs has thus far been elusive. We prove that SGD minimizes an average potential over the posterior distribution of weights along with an entropic regularization term. This potential is however not the original loss function in general. So SGD does perform variational inference, but for a different loss than the one used to compute the gradients. Even more surprisingly, SGD does not even converge in the classical sense: we show that the most likely trajectories of SGD for deep networks do not behave like Brownian motion around critical points. Instead, they resemble closed loops with deterministic components. We prove that such out-of-equilibrium behavior is a consequence of highly non-isotropic gradient noise in SGD; the covariance matrix of mini-batch gradients for deep networks has a rank as small as 1% of its dimension. We provide extensive empirical validation of these claims, proven in the appendix.

SGD隐含地执行了变分推理；梯度噪声是高度非各向异性的，因此SGD甚至不能收敛到原始损失的临界点。

本文对SGD作为一个非平衡过程进行了变分分析。

本文讨论了神经网络背景下标准SGD最小化的正则化目标函数，并利用Fokker-Planck方程提供了一个变分推理的视角。

开发了一种理论，研究随机梯度噪声对SGD的影响，特别是对深度神经网络模型的影响。


The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill policy with a novel forward consistency loss. In our framework, the role of the expert is only to communicate the goals (i.e., what to imitate) during inference. The learned policy is then employed to mimic the expert (i.e., how to imitate) after seeing just a sequence of images demonstrating the desired task. Our method is 'zero-shot' in the sense that the agent never has access to expert actions during training or for the task demonstration at inference. We evaluate our zero-shot imitator in two real-world settings: complex rope manipulation with a Baxter robot and navigation in previously unseen office environments with a TurtleBot. Through further experiments in VizDoom simulation, we provide evidence that better mechanisms for exploration lead to learning a more capable policy which in turn improves end task performance. Videos, models, and more details are available at https://pathak22.github.io/zeroshot-imitation/.

代理在训练时不需要任何形式的监督，从自己的经验中学习后，可以在测试时学习模仿单纯的视觉演示（没有动作）。

本文提出了通过学习参数化的技能函数进行零样本视觉学习的方法。

一篇关于模仿推理过程中刚刚提出的任务的论文，其中学习是以自我监督的方式进行的，在训练过程中，代理探索相关但不同的任务。

提出了一种避开昂贵的专家示范问题的方法，即利用代理的随机探索来学习无需特定预训练就能应用的通用技能。


Distributional Semantics Models(DSM) derive word space from linguistic items  in context. Meaning is obtained by defining a distance measure between vectors  corresponding to lexical entities. Such vectors present several problems. This  work concentrates on quality of word embeddings, improvement of word embedding  vectors, applicability of a novel similarity metric used ‘on top’ of the  word embeddings. In this paper we provide comparison between two methods  for post process improvements to the baseline DSM vectors. The counter-fitting  method which enforces antonymy and synonymy constraints into the Paragram  vector space representations recently showed improvement in the vectors’ capability  for judging semantic similarity. The second method is our novel RESM  method applied to GloVe baseline vectors. By applying the hubness reduction  method, implementing relational knowledge into the model by retrofitting synonyms  and providing a new ranking similarity definition RESM that gives maximum  weight to the top vector component values we equal the results for the ESL  and TOEFL sets in comparison with our calculations using the Paragram and Paragram  + Counter-fitting methods. For SIMLEX-999 gold standard since we cannot  use the RESM the results using GloVe and PPDB are significantly worse compared  to Paragram. Apparently, counter-fitting corrects hubness. The Paragram  or our cosine retrofitting method are state-of-the-art results for the SIMLEX-999  gold standard. They are 0.2 better for SIMLEX-999 than word2vec with sense  de-conflation (that was announced to be state-of the-art method for less reliable  gold standards). Apparently relational knowledge and counter-fitting is more important  for judging semantic similarity than sense determination for words. It is to  be mentioned, though that Paragram hyperparameters are fitted to SIMLEX-999  results. The lesson is that many corrections to word embeddings are necessary  and methods with more parameters and hyperparameters perform better.

论文描述了一个加强词向量空间模型的过程，并对Paragram和GloVe模型的相似度基准进行了评估。

本文提出了一种新的算法，调整GloVe词向量，然后使用它们之间的非欧几里得相似度函数。

作者提出了对现有向量空间模型弱点的看法，并列出了完善现有词汇向量的六步方法


Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. For applications on the server with large scale concurrent requests, the latency during inference can also be very critical for costly computing resources. In this work, we address these problems by quantizing the network, both weights and activations, into multiple binary codes {-1,+1}. We formulate the quantization as an optimization problem. Under the key observation that once the quantization coefficients are fixed the binary codes can be derived efficiently by binary search tree, alternating minimization is then applied.   We test the quantization for two well-known RNNs, i.e., long short term memory (LSTM) and gated recurrent unit (GRU), on the language models. Compared with the full-precision counter part, by 2-bit quantization we can achieve ~16x memory saving and  ~6x real inference acceleration on CPUs, with only a reasonable loss in the accuracy. By 3-bit quantization, we can achieve almost no loss in the accuracy or even surpass the original model, with ~10.5x memory saving and ~3x real inference acceleration. Both results beat the exiting quantization works with large margins.   We extend our alternating quantization to image classification tasks. In both RNNs and feedforward neural networks, the method also achieves  excellent performance.

我们提出了一种新的量化方法，并将其应用于RNN的量化，以实现压缩和加速。

本文提出了一种用于循环神经网络的多位量化方法。

一种量化神经网络权重矩阵的技术，以及一种交替优化过程，以估计最能代表原始向量的k个二进制向量和系数的集合。


The goal of this paper is to demonstrate a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for linear layers in a neural network and test this implementation on the CIFAR-10 dataset. The proposed method outperforms factorization using tensor trains, providing greater compression for the same level of accuracy and greater accuracy for the same level of compression. We demonstrate MERA-layers with 3900 times fewer parameters and a reduction in accuracy of less than 1% compared to the equivalent fully connected layers.

我们用多尺度纠缠重正化假定取代了神经网络的全连接层，这是一种描述长距离相关的量子操作。

在这篇论文中，作者建议使用MERA张量技术来压缩神经网络。

用于神经网络的线性映射的新参数化，使用线性图的分层因子化，减少了参数的数量，同时仍允许对相对复杂的相互作用进行建模。

研究利用低秩张量分解压缩前馈层，探索树状分解的方法


Deep learning models have outperformed traditional methods in many fields such  as natural language processing and computer vision. However, despite their  tremendous success, the methods of designing optimal Convolutional Neural Networks  (CNNs) are still based on heuristics or grid search. The resulting networks  obtained using these techniques are often overparametrized with huge computational  and memory requirements. This paper focuses on a structured, explainable  approach towards optimal model design that maximizes accuracy while keeping  computational costs tractable. We propose a single-shot analysis of a trained CNN  that uses Principal Component Analysis (PCA) to determine the number of filters  that are doing significant transformations per layer, without the need for retraining.  It can be interpreted as identifying the dimensionality of the hypothesis space  under consideration. The proposed technique also helps estimate an optimal number  of layers by looking at the expansion of dimensions as the model gets deeper.  This analysis can be used to design an optimal structure of a given network on  a dataset, or help to adapt a predesigned network on a new dataset. We demonstrate  these techniques by optimizing VGG and AlexNet networks on CIFAR-10,  CIFAR-100 and ImageNet datasets.

我们提出了对训练有素的神经网络的单次分析，以消除冗余并确定最佳网络结构

本文提出了一套启发式方法，用于识别一个好的神经网络架构，基于数据集上的单元激活的PCA

本文提出了一个通过识别跨层的冗余过滤器来优化神经网络结构的框架。


Recent work has introduced attacks that extract the architecture information of deep neural networks (DNN), as this knowledge enhances an adversary’s capability to conduct attacks on black-box networks. This paper presents the first in-depth security analysis of DNN fingerprinting attacks that exploit cache side-channels.   First, we define the threat model for these attacks:  our adversary does not need the ability to query the victim model; instead, she runs a co-located process on the host machine victim ’s deep learning  (DL) system is running and passively monitors the accesses of the target functions in the shared framework.   Second, we introduce DeepRecon, an attack that reconstructs the architecture of the victim network by using the internal information extracted via Flush+Reload, a cache side-channel technique. Once the attacker observes function invocations that map directly to architecture attributes of the victim network, the attacker can reconstruct the victim’s entire network architecture.   In our evaluation, we demonstrate that an attacker can accurately reconstruct two complex networks (VGG19 and ResNet50) having only observed one forward propagation. Based on the extracted architecture attributes, we also demonstrate that an attacker can build a meta-model that accurately fingerprints the architecture and family of the pre-trained model in a transfer learning setting. From this meta-model,  we evaluate the importance of the observed attributes in the fingerprinting process. Third, we propose and evaluate new framework-level defense techniques that obfuscate our attacker’s observations. Our empirical security analysis represents a step toward understanding the DNNs’ vulnerability to cache side-channel attacks.

我们对利用缓存侧信道的DNN指纹攻击进行了首次深入的安全分析，这代表着向了解DNN对侧信道攻击的脆弱性迈出了一步。

本文考虑了利用缓存侧信道对神经网络架构进行指纹识别的问题，并讨论了通过不安全防御的安全性。

本文进行了缓存侧信道攻击，以提取受害者模型的属性并推断其架构，同时表明它们可以达到几乎完美的分类精度。


Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes. We argue that, in addition to the primary objective, training also using a complement objective that leverages information from the complement classes can be effective in improving model performance. This motivates us to study a new training paradigm that maximizes the likelihood of the ground-truth class while neutralizing the probabilities of the complement classes. We conduct extensive experiments on multiple tasks ranging from computer vision to natural language understanding. The experimental results confirm that, compared to the conventional training with just one primary objective, training also with the complement objective further improves the performance of the state-of-the-art models across all tasks. In addition to the accuracy improvement, we also show that models trained with both primary and complement objectives are more robust to single-step adversarial attacks.

我们提出了补充目标训练（COT），这是一种新的训练范式，可以优化主要目标和补充目标，以有效学习神经网络的参数。

考虑用 "补充 "目标最大化来增强交叉熵目标，其目的是中和除地面真实标签之外的其他类别的预测概率。

作者提出了一个基于评估从不正确的类中收集的信息的softmax最小化的次要目标，导致了一种新的训练方法。

处理使用跨熵损失的分类或序列生成任务的神经网络的训练问题


We present a new method for uncertainty estimation and out-of-distribution detection in neural networks with softmax output. We extend softmax layer with an additional constant input. The corresponding additional output is able to represent the uncertainty of the network. The proposed method requires neither additional parameters nor multiple forward passes nor input preprocessing nor out-of-distribution datasets. We show that our method performs comparably to more computationally expensive methods and outperforms baselines on our experiments from image recognition and sentiment analysis domains.

在没有额外的可学习参数的情况下，在单一的前向传递中进行不确定性估计。

一种计算用于分类问题的DNN的输出不确定性估计的新方法，与最先进的不确定性估计方法相匹配，并在分布外检测任务中优于它们。

作者提出了抑制性softmax，即通过增加一个常数激活对softmax进行修改，从而提供一个不确定性的衡量标准。


When deep learning is applied to sensitive data sets, many privacy-related implementation issues arise. These issues are especially evident in the healthcare, finance, law and government industries. Homomorphic encryption could allow a server to make inferences on inputs encrypted by a client, but to our best knowledge, there has been no complete implementation of common deep learning operations, for arbitrary model depths, using homomorphic encryption. This paper demonstrates a novel approach, efficiently implementing many deep learning functions with bootstrapped homomorphic encryption. As part of our implementation, we demonstrate Single and Multi-Layer Neural Networks, for the Wisconsin Breast Cancer dataset, as well as a Convolutional Neural Network for MNIST. Our results give promising directions for privacy-preserving representation learning, and the return of data control to users.

我们做了一个功能丰富的深度学习系统，用加密的输入，产生加密的输出，保护了隐私。

一个使用FHE方案的私有深度学习模型推理框架，支持快速引导，从而可以减少计算时间。

本文提出了一种使用同态加密安全评估神经网络的方法。


In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportunity to explore theorem proving with human supervision. We use GamePad to synthesize proofs for a simple algebraic rewrite problem and train baseline models for a formalization of the Feit-Thompson theorem. We address position evaluation (i.e., predict the number of proof steps left) and tactic prediction (i.e., predict the next proof step) tasks, which arise naturally in tactic-based theorem proving.

我们介绍了一个名为GamePad的系统，以探索机器学习方法在Coq证明助手中的定理证明的应用。

本文描述了一个将机器学习应用于交互式定理证明的系统，重点讨论了战术预测和位置评估的任务，并表明神经模型在这两项任务上的表现优于SVM。

建议使用机器学习技术来帮助在定理检验器Coq中构建证明。


Deep neural networks are usually huge, which significantly limits the deployment on low-end devices. In recent years, many  weight-quantized models have  been proposed. They have small storage and fast inference, but training can still be time-consuming. This can be improved with distributed learning. To reduce the high communication cost due to worker-server synchronization, recently gradient quantization has also been proposed to train deep networks with full-precision weights.   In this paper, we theoretically study how the combination of both weight and gradient quantization affects convergence.  We show  that (i) weight-quantized models converge to an error related to the weight quantization resolution and weight dimension; (ii) quantizing gradients slows convergence by a factor related to the gradient quantization resolution and dimension; and (iii) clipping the gradient before quantization renders this factor dimension-free, thus allowing the use of fewer bits for gradient quantization. Empirical experiments confirm the theoretical convergence results, and demonstrate that quantized networks can speed up training and have comparable performance as full-precision networks.

在本文中，我们从理论上和经验上研究了在分布式环境中对具有量化梯度的损失感知权重量化网络的高效训练。

本文研究了分布式环境下不同梯度精度的损失意识权重量化的收敛特性，并提供了全精度、量化和量化剪切梯度的权重量化的收敛分析。

作者提出分析在完全同步的分布式环境下训练参数化模型时，同时量化权重和梯度的效果。


Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for future tasks to be added and thus leave enough capacity for them. To achieve Selfless Sequential Learning we study different regularization strategies and activation functions. We find that  imposing sparsity at the level of the representation (i.e. neuron activations) is more beneficial for sequential learning than encouraging parameter sparsity. In particular, we propose a novel regularizer, that encourages representation sparsity by means of neural inhibition. It results in few active neurons which in turn leaves more free neurons to be utilized by upcoming tasks. As neural inhibition over an entire layer can be too drastic, especially for complex tasks requiring strong representations,  our regularizer only inhibits other neurons in a local neighbourhood, inspired by lateral inhibition processes in the brain. We combine our novel regularizer with state-of-the-art lifelong learning methods that penalize changes to important previously learned parts of the network. We show that our new regularizer leads to increased sparsity which translates in consistent performance improvement on diverse datasets.

提高顺序学习性能的正则化策略

一种新的、基于正则化的方法来解决顺序学习问题，使用固定大小的模型，为损失增加额外的项，鼓励表示的稀疏性和打击灾难性的遗忘。

本文通过提出规范化的学习策略来处理终身学习中的灾难性遗忘问题


A Synaptic Neural Network (SynaNN) consists of synapses and neurons. Inspired by the synapse research of neuroscience, we built a synapse model with a nonlinear synapse function of excitatory and inhibitory channel probabilities. Introduced the concept of surprisal space and constructed a commutative diagram, we proved that the inhibitory probability function -log(1-exp(-x)) in surprisal space is the topologically conjugate function of the inhibitory complementary probability 1-x in probability space. Furthermore, we found that the derivative of the synapse over the parameter in the surprisal space is equal to the negative Bose-Einstein distribution. In addition, we constructed a fully connected synapse graph (tensor) as a synapse block of a synaptic neural network. Moreover, we proved the gradient formula of a cross-entropy loss function over parameters, so synapse learning can work with the gradient descent and backpropagation algorithms. In the proof-of-concept experiment, we performed an MNIST training and testing on the MLP model with synapse network as hidden layers.

一个具有突触图和学习的突触神经网络，它具有拓扑共轭和玻色-爱因斯坦分布在surprisal空间的特点。

作者提出了一种由突触图组成的混合神经工作，可以嵌入到标准神经网络中。

提出了一个基于真实细胞膜的兴奋性和抑制性离子通道的生物启发的神经网络模型


Many types of relations in physical, biological, social and information systems can be modeled as homogeneous or heterogeneous concept graphs. Hence, learning from and with graph embeddings has drawn a great deal of research interest recently, but only ad hoc solutions have been obtained this far. In this paper, we conjecture that the one-shot supervised learning mechanism is a bottleneck in improving the performance of the graph embedding learning algorithms, and propose to extend this by introducing a multi-shot unsupervised learning framework. Empirical results on several real-world data set show that the proposed model consistently and significantly outperforms existing state-of-the-art approaches on knowledge base completion and graph based multi-label classification tasks.

一种广义的知识图谱嵌入方法，基于三个不同的同时目标来学习嵌入，其性能与现有的最先进的方法相当，甚至超过了现有的方法。

解决了使用神经网络学习多关系图的嵌入的任务

提出了一种新的方法GEN，来计算多关系图的嵌入，特别是所谓的E-Cells和R-Cells可以回答(h,r,?),(?r,t)和(h,?,t)形式的查询


We introduce and study minimax curriculum learning (MCL), a new method for adaptively selecting a sequence of training subsets for a succession of stages in machine learning. The subsets are encouraged to be small and diverse early on, and then larger, harder, and allowably more homogeneous in later stages. At each stage, model weights and training sets are chosen by solving a joint continuous-discrete minimax optimization, whose objective is composed of a continuous loss (reflecting training set hardness) and a discrete submodular promoter of diversity for the chosen subset. MCL repeatedly solves a sequence of such optimizations with a schedule of increasing training set size and decreasing pressure on diversity encouragement. We reduce MCL to the minimization of a surrogate function handled by submodular maximization and continuous gradient methods. We show that MCL achieves better performance and, with a clustering trick, uses fewer labeled samples for both shallow and deep models while achieving the same performance. Our method involves repeatedly solving constrained submodular maximization of an only slowly varying function on the same ground set. Therefore, we develop a heuristic method that utilizes the previous submodular maximization solution as a warm start for the current submodular maximization process to reduce computation while still yielding a guarantee.

Minimax Curriculum Learning是一种机器教学方法，涉及增加理想的硬度和预定减少多样性。

一种课程学习方法，使用子模块函数来捕捉训练期间选择的例子的多样性。

本文介绍了MiniMax Curriculum学习，作为一种通过为其提供不同的数据子集来适应性地训练模型的方法。


Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factors cause major human diseases. In this work, we focus on two challenges in particular: How do we build richer causal models, which can capture highly nonlinear relationships and interactions between multiple causes? How do we adjust for latent confounders, which are variables influencing both cause and effect and which prevent learning of causal relationships? To address these challenges, we synthesize ideas from causality and modern probabilistic modeling. For the first, we describe implicit causal models, a class of causal models that leverages neural architectures with an implicit density. For the second, we describe an implicit causal model that adjusts for confounders by sharing strength across examples. In experiments, we scale Bayesian inference on up to a billion genetic measurements. We achieve state of the art accuracy for identifying causal factors: we significantly outperform the second best result by an absolute difference of 15-45.3%.

应用于因果关系和遗传学的隐性模型

作者提出使用隐含模型来解决全基因组关联问题。

本文针对全基因组关联研究中由于种群结构造成的混杂和基因组不同部分之间可能存在的非线性相互作用问题提出了解决方案，并在统计遗传学和ML之间建立了桥梁。

提出了一个用于GWAS的非线性生成模型，该模型对种群结构进行建模，其中非线性的建模采用神经网络作为非线性函数近似器，推理采用无似然变分推理进行。


Few-shot learning trains image classifiers over datasets with few examples per category.   It poses challenges for the optimization algorithms, which typically require many examples to fine-tune the model parameters for new categories.   Distance-learning-based approaches avoid the optimization issue by embedding the images into a metric space and applying the nearest neighbor classifier for new categories. In this paper, we propose to exploit the object-level relation to learn the image relation feature, which is converted into a distance directly.  For a new category, even though its images are not seen by the model, some objects may appear in the training images. Hence, object-level relation is useful for inferring the relation of images from unseen categories. Consequently, our model generalizes well for new categories without fine-tuning.  Experimental results on benchmark datasets show that our approach outperforms state-of-the-art methods.

通过利用对象级的关系来学习图像层面的关系（相似性），进行少样本学习

本文通过提出一种基于嵌入的方法来处理少样本学习的问题，该方法可以学习比较支持和查询集例子之间的对象级特征。

提出了一种基于近邻搜索，利用不同图像之间的对象级关系，将两张输入图像的特征图串联成一张特征图的少样本学习方法。


Word embeddings are widely used in machine learning based natural language processing systems. It is common to use pre-trained word embeddings which provide benefits such as reduced training time and improved overall performance. There has been a recent interest in applying natural language processing techniques to programming languages. However, none of this recent work uses pre-trained embeddings on code tokens. Using extreme summarization as the downstream task, we show that using pre-trained embeddings on code tokens provides the same benefits as it does to natural languages, achieving: over 1.9x speedup, 5\% improvement in test loss, 4\% improvement in F1 scores, and resistance to over-fitting. We also show that the choice of language used for the embeddings does not have to match that of the task to achieve these benefits and that even embeddings pre-trained on human languages provide these benefits to programming languages.

探索应用于源代码的自然语言处理技术的研究人员没有使用任何形式的预训练嵌入，我们表明他们应该这样做。

本文旨在了解通过使用类似NLP的语言模型对编程语言代码的词嵌入进行预训练是否会对极端的代码总结任务产生影响。

这项工作显示了如何利用代码语料库预训练单词向量，从而获得比随机初始化和训练的表征更适合于函数/方法名称预测的表征。


Recently, Approximate Policy Iteration (API) algorithms have achieved super-human proficiency in two-player zero-sum games such as Go, Chess, and Shogi without human data. These API algorithms iterate between two policies: a slow policy (tree search), and a fast policy (a neural network). In these two-player games, a reward is always received at the end of the game. However, the Rubik’s Cube has only a single solved state, and episodes are not guaranteed to terminate. This poses a major problem for these API algorithms since they rely on the reward received at the end of the game. We introduce Autodidactic Iteration: an API algorithm that overcomes the problem of sparse rewards by training on a distribution of states that allows the reward to propagate from the goal state to states farther away. Autodidactic Iteration is able to learn how to solve the Rubik’s Cube and the 15-puzzle without relying on human data. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves — less than or equal to solvers that employ human domain knowledge.

我们用纯强化学习解决Rubik's Cube问题

使用强化学习（RL）与蒙特卡洛树搜索（MCTS）通过自律迭代解决Rubik cube问题。

这项工作使用一种叫做Autodidactic迭代的近似策略迭代方法来解决Rubik's Cube ，通过创建自己的奖励系统来克服稀疏的奖励问题。

介绍了一种解决Rubik's cube的深度RL算法，该算法可以处理Rubik's cube的巨大状态空间和非常稀疏的奖励。


Answering compositional questions requiring multi-step reasoning is challenging for current models. We introduce an end-to-end differentiable model for interpreting questions, which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a knowledge graph, together with a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituents, culminating in a grounding for the complete sentence which is an answer to the question. For example, to interpret ‘not green’, the model will represent ‘green’ as a set of entities, ‘not’ as a trainable ungrounded vector, and then use this vector to parametrize a composition function to perform a complement operation. For each sentence, we build a parse chart subsuming all possible parses, allowing the model to jointly learn both the composition operators and output structure by gradient descent. We show the model can learn to represent a variety of challenging semantic operators, such as quantifiers, negation, disjunctions and composed relations on a synthetic question answering task. The model also generalizes well to longer sentences than seen in its training data, in contrast to LSTM and RelNet baselines. We will release our code.

我们描述了一个端到端的可微的问答模型，该模型通过学习构成的神经模块和句子的句法结构，学习将问题中的文本跨度表示为知识图谱中的指称。

本文提出了一个用于视觉问答的模型，它可以学习模块化神经网络的参数和结构预测器，不需要监督结构或句法分析器的帮助。

提出通过学习捕捉句法和学习词的语义的潜在树，从答案和KB中训练问答模型。


Deep learning software demands reliability and performance. However, many of the existing deep learning frameworks are software libraries that act as an unsafe DSL in Python and a computation graph interpreter. We present DLVM, a design and implementation of a compiler infrastructure with a linear algebra intermediate representation, algorithmic differentiation by adjoint code generation, domain- specific optimizations and a code generator targeting GPU via LLVM. Designed as a modern compiler infrastructure inspired by LLVM, DLVM is more modular and more generic than existing deep learning compiler frameworks, and supports tensor DSLs with high expressivity. With our prototypical staged DSL embedded in Swift, we argue that the DLVM system enables a form of modular, safe and performant frameworks for deep learning.

我们介绍了一种新颖的编译器基础设施，解决了现有深度学习框架的缺陷。

提出在深度学习引擎中从临时性的代码生成转向编译器和语言的最佳实践。

本文提出了一个编译器框架，允许为深度学习系统定义特定领域的语言，并定义了可以利用标准优化和神经网络的专门优化的编译阶段。

本文介绍了一个DLVM，以利用张量编译器的编译器方面的优势