We introduce CBF, an exploration method that works in the absence of rewards or end of episode signal.CBF is based on intrinsic reward derived from the error of a dynamics model operating in feature space.It was inspired by (Pathak et al., 2017), is easy to implement, and can achieve results such as passing four levels of Super Mario Bros, navigating VizDoom mazes and passing two levels of SpaceInvaders.We investigated the effect of combining the method with several auxiliary tasks, but find inconsistent improvements over the CBF baseline.

一种简单的内在激励方法，使用策略的特征空间中的前向动力学模型误差。


This paper is concerned with the robustness of VAEs to adversarial attacks.We highlight that conventional VAEs are brittle under attack but that methods recently introduced for disentanglement such as Î²-TCVAE (Chen et al., 2018) improve robustness, as demonstrated through a variety of previously proposed adversarial attacks (Tabacof et al. (2016); Gondim-Ribeiro et al. (2018); Kos et al.(2018)).This motivated us to develop Seatbelt-VAE, a new hierarchical disentangled VAE that is designed to be significantly more robust to adversarial attacks than existing approaches, while retaining high quality reconstructions.

我们表明，对于旨在欺骗它们将对抗性输入解码为选定目标的对抗性攻击，分解的VAE比虚构的VAE更加稳健。然后，我们开发了一个更加稳健的分层分解VAE，即Seatbelt-VAE。

作者提出了一个新的VAE模型，称为 "Seatbelt-VAE"，显示出对潜伏攻击比基准更加稳健。


The backpropagation algorithm is the de-facto standard for credit assignment in artificial neural networks due to its empirical results.Since its conception, variants of the backpropagation algorithm have emerged.More specifically, variants that leverage function changes in the backpropagation equations to satisfy their specific requirements.Feedback Alignment is one such example, which replaces the weight transpose matrix in the backpropagation equations with a random matrix in search of a more biologically plausible credit assignment algorithm.In this work, we show that function changes in the  backpropagation procedure is equivalent to adding an implicit learning rate to an artificial neural network.Furthermore, we learn activation function derivatives in the backpropagation equations to demonstrate early convergence in these artificial neural networks.Our work reports competitive performances with early convergence on MNIST and CIFAR10 on sufficiently large deep neural network architectures.

我们证明，反向传播中的函数变化相当于一个隐性学习率


Unsupervised text style transfer is the task of re-writing text of a given style into a target style without using a parallel corpus of source style and target style sentences for training.Style transfer systems are evaluated on their ability to generate sentences that1) possess the target style,2) are fluent and natural sounding, and3) preserve the non-stylistic parts (content) of the source sentence.We train a reinforcement learning (RL) based unsupervised style transfer system that incorporates rewards for the above measures, and describe novel rewards shaping methods for the same.Our approach does not attempt to disentangle style and content, and leverages the power of massively pre-trained language models as well as the Transformer.Our system significantly outperforms existing state-of-art systems based on human as well as automatic evaluations on target style, fluency and content preservation as well as on overall success of style transfer, on a variety of datasets.

文本风格转移的强化学习方法

介绍了一种基于RL的方法，该方法利用预先训练好的语言模型来转移文本风格，没有脱离目标，同时使用来自另一个模型的风格转移代系。

作者提出了一个由流畅性、内容和风格组成的组合奖励，用于文本风格的转移。


Despite the success of Generative Adversarial Networks (GANs) in image synthesis, there lacks enough understanding on what networks have learned inside the deep generative representations and how photo-realistic images are able to be composed from random noises.In this work, we show that highly-structured semantic hierarchy emerges from the generative representations as the variation factors for synthesizing scenes.By probing the layer-wise representations with a broad set of visual concepts at different abstraction levels, we are able to quantify the causality between the activations and the semantics occurring in the output image.Such a quantification identifies the human-understandable variation factors learned by GANs to compose scenes.The qualitative and quantitative results suggest that the generative representations learned by GAN are specialized to synthesize different hierarchical semantics: the early layers tend to determine the spatial layout and configuration, the middle layers control the categorical objects, and the later layers finally render the scene attributes as well as color scheme.Identifying such a set of manipulatable latent semantics facilitates semantic scene manipulation.

我们表明，高度结构化的语义层次出现在深度生成表征中，作为合成场景的结果。

论文研究了输入到StyleGAN不同层的潜变量所编码的方面。

本文对StyleGAN的生成器中的卷积层在布局、场景类别、场景属性和颜色上的激活进行了视觉引导的解释。


Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries.However, these VAEs are hindered by the non-unique nature of SMILES strings and the computational cost of graph convolutions.To efficiently pass messages along all paths through the molecular graph, we encode multiple SMILES strings of a single molecule using a set of stacked recurrent neural networks, harmonizing hidden representations of each atom between SMILES representations, and use attentional pooling to build a final fixed-length latent representation.By then decoding to a disjoint set of SMILES strings of the molecule, our All SMILES VAE learns an almost bijective mapping between molecules and latent representations near the high-probability-mass subspace of the prior.Our SMILES-derived but molecule-based latent representations significantly surpass the state-of-the-art in a variety of fully- and semi-supervised property regression and molecular property optimization tasks.

我们在同一分子的多个SMILES字符串中汇集信息，沿分子图的所有路径传递信息，产生的潜在表征在各种任务中大大超过了最先进的水平。

该方法使用SMILES字符串的多个输入，在这些字符串中进行特征融合，并通过SMILES字符串的多个输出目标进行网络训练，创建一个独立于SMILES变化的稳健的固定长度的潜在表示。

作者描述了一种类似于分子变分自动编码器的新方法，该方法将分子编码为字符串，以减少分子中各原子间共享信息所需的操作。


We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative  Adversarial  Network (cGAN).Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code.To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes.The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives.Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity.We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction.We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task.

我们提出了一个简单而通用的方法，避免了各种条件GAN中的模式崩溃问题。

本文为条件GAN目标提出了一个正则化项，以促进多样化的多模态生成并防止模式崩溃。

本文提出了一种为各种条件性GAN框架生成多样化输出的方法，包括图像到图像的转换、图像绘画和视频预测，可以应用于各种条件性合成框架，用于各种任务。


The transformer is a state-of-the-art neural translation model that uses attention to iteratively refine lexical representations with information drawn from the surrounding context.Lexical features are fed into the first layer and propagated through a deep network of hidden layers.We argue that the need to represent and propagate lexical features in each layer limits the modelâ€™s capacity for learning and representing other information relevant to the task.To alleviate this bottleneck, we introduce gated shortcut connections between the embedding layer and each subsequent layer within the encoder and decoder.This enables the model to access relevant lexical content dynamically, without expending limited resources on storing it within intermediate states.We show that the proposed modification yields consistent improvements on standard WMT translation tasks and reduces the amount of lexical information passed along the hidden layers.We furthermore evaluate different ways to integrate lexical connections into the transformer architecture and present ablation experiments exploring the effect of proposed shortcuts on model behavior.

为转化器模型配备通往嵌入层的捷径，释放了模型的能力以学习新信息。


Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions.  More recent generative models leverage the power of neural networks to implicitly learn and represent probability models over complex images.  We describe methods to extract explicit probability density estimates from GANs, and explore the properties of these image density functions.  We perform sanity check experiments to provide evidence that these probabilities are reasonable.  However, we also show that density functions of natural images are difficult to interpret and thus limited in use.  We study reasons for this lack of interpretability, and suggest that we can get better interpretability by doing density estimation on latent representations of images.

我们研究了不可逆转的GAN中概率密度值和图像内容之间的关系。

作者试图在GAN的帮助下估计图像的概率分布，并在潜伏空间中开发一个适当的PDF近似。


Convolutional Neural Networks (CNNs) are composed of multiple convolution layers and show elegant performance in vision tasks.The design of the regular convolution is based on the Receptive Field (RF) where the information within a specific region is processed.In the view of the regular convolution's RF, the outputs of neurons in lower layers with smaller RF are bundled to create neurons in higher layers with larger RF. As a result, the neurons in high layers are able to capture the global context even though the neurons in low layers only see the local information.However, in lower layers of the biological brain, the information outside of the RF changes the properties of neurons.In this work, we extend the regular convolution and propose spatially shuffled convolution (ss convolution).In ss convolution, the regular convolution is able to use the information outside of its RF by spatial shuffling which is a simple and lightweight operation.We perform experiments on CIFAR-10 and ImageNet-1k dataset, and show that ss convolution improves the classification performance across various CNNs.

我们提出了空间洗牌卷积，即常规卷积包含了来自其接受领域之外的信息。

提出了使用其RF以外的信息的SS convulation，在对多个CNN模型进行测试时显示了更好的结果。

作者提出了一种卷积神经网络中卷积层的洗牌战略。


We propose a framework to model the distribution of sequential data coming froma set of entities connected in a graph with a known topology.The method isbased on a mixture of shared hidden Markov models (HMMs), which are trainedin order to exploit the knowledge of the graph structure and in such a way that theobtained mixtures tend to be sparse.Experiments in different application domainsdemonstrate the effectiveness and versatility of the method.

一种对来自图连接实体的序列的生成分布进行建模的方法。

作者提出了一种方法，使用HMM的共同池的混合物对来自多个相互联系的源的顺序数据进行建模。


To gain high rewards in muti-agent scenes, it is sometimes necessary to understand other agents and make corresponding optimal decisions.We can solve these tasks by first building models for other agents and then finding the optimal policy with these models.To get an accurate model, many observations are needed and this can be sample-inefficient.What's more, the learned model and policy can overfit to current agents and cannot generalize if the other agents are replaced by new agents.In many practical situations, each agent we face can be considered as a sample from a population with a fixed but unknown distribution.Thus we can treat the task against some specific agents as a task sampled from a task distribution.We apply meta-learning method to build models and learn policies.Therefore when new agents come, we can adapt to them efficiently.Experiments on grid games show that our method can quickly get high rewards.

我们的工作将元学习应用于多代理强化学习，以帮助我们的代理有效地适应新到来的对手。

本文的重点是使用基于MAML的方法快速适应环境中其他代理的新行为。

本文提出了一种基于模型无关的元学习框架的多代理学习方法，用于多代理RL的对手建模任务。


We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation.  This characterization also leads to an algorithm for projecting a convolutional layer onto an operator-norm ball.We show that this is an effective regularizer;  for example, it improves the test error of a deep residual network using batch normalization on CIFAR-10 from 6.2% to 5.3%.

我们描述了与标准二维多通道卷积层相关的线性变换的奇异值，使其能够得到有效计算。

本文致力于计算卷积层的奇异值

推导出计算深度神经网络卷积层奇异值的精确公式，并表明通过诉诸快速FFT变换，计算奇异值可以比计算卷积矩阵的全部SVD快得多。


Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning.A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment.Computing a Bayes-optimal policy is however intractable for all but the smallest tasks.In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn to perform approximate inference in an unknown environment, and incorporate task uncertainty directly during action selection.In a grid-world domain, we illustrate how variBAD performs structured online exploration as a function of task uncertainty.We also evaluate variBAD on MuJoCo domains widely used in meta-RL and show that it achieves higher return during training than existing methods.

VariBAD利用元学习、贝叶斯RL和近似变分推理的思想，为深度RL开辟了一条可操作的近似贝叶斯最优探索之路。

本文提出了一种新的深度强化学习方法，可以有效地权衡探索和利用，它结合了元学习、变分推理和贝叶斯RL。


In a continual learning setting, new categories may be introduced over time, and an ideal learning system should perform well on both the original categories and the new categories.While deep neural nets have achieved resounding success in the classical setting, they are known to forget about knowledge acquired in prior episodes of learning if the examples encountered in the current episode of learning are drastically different from those encountered in prior episodes.This makes deep neural nets ill-suited to continual learning.In this paper, we propose a new model that can both leverage the expressive power of deep neural nets and is resilient to forgetting when new categories are introduced.We demonstrate an improvement in terms of accuracy on original classes compared to a vanilla deep neural net.

我们表明，指标学习可以帮助减少灾难性遗忘的发生

本文通过提高最后一层的表现力，应用度量学习来减少神经网络上的灾难性遗忘，从而在持续学习中获得更好的结果。


Biomedical knowledge bases are crucial in modern data-driven biomedical sciences, but auto-mated biomedical knowledge base construction remains challenging.In this paper, we consider the problem of disease entity normalization, an essential task in constructing a biomedical knowledge base.  We present NormCo, a deep coherence model which considers the semantics of an entity mention, as well as the topical coherence of the mentions within a single document.NormCo mod-els entity mentions using a simple semantic model which composes phrase representations from word embeddings, and treats coherence as a disease concept co-mention sequence using an RNN rather than modeling the joint probability of all concepts in a document, which requires NP-hard inference.  To overcome the issue of data sparsity, we used distantly supervised data and synthetic data generated from priors derived from the BioASQ dataset.  Our experimental results show thatNormCo outperforms state-of-the-art baseline methods on two disease normalization corpora in terms of (1) prediction quality and (2) efficiency, and is at least as performant in terms of accuracy and F1 score on tagged documents.

我们提出了NormCo，一个深度一致性模型，它考虑了实体提及的语义，以及单个文档中提及的主题一致性，以进行疾病实体的规范化。

使用GRU自动编码器来表示 "上下文"（在一个句子的范围内与某一疾病相关的内容），解决了BioNLP的任务，比最知名的方法有明显的改进。


We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.Multiplicative interaction layers as primitive operations have a long-established presence in the literature, though this often not emphasized and thus under-appreciated.We begin by showing that such layers strictly enrich the representable function classes of neural networks.We conjecture that multiplicative interactions offer a particularly powerful inductive bias when fusing multiple streams of information or when conditional computation is required.We therefore argue that they should be considered in many situation where multiple compute or information paths need to be combined, in place of the simple and oft-used concatenation operation.Finally, we back up our claims and demonstrate the potential of multiplicative interactions by applying them in large-scale complex RL and sequence modelling tasks, where their use allows us to deliver state-of-the-art results, and thereby provides new evidence in support of multiplicative interactions playing a more prominent role when designing new neural network architectures.

我们探索乘法互动的作用，将其作为一个统一的框架来描述一系列经典和现代的神经网络架构模式，如门控、注意力层、超网络和动态卷积等。

提出了乘法交互作为代表常用模型架构设计组件的统一表征，显示了在RL和序列建模等任务上的卓越性能的实证证明。

本文探讨了不同类型的乘法互动，发现MI模型能够在语言建模和强化学习问题上取得最先进的性能。


Developing conditional generative models for text-to-video synthesis is an extremely challenging yet an important topic of research in machine learning.In this work, we address this problem by introducing Text-Filter conditioning Generative Adversarial Network (TFGAN), a GAN model with novel conditioning scheme that aids improving the text-video associations.With a combination of this conditioning scheme and a deep GAN architecture, TFGAN generates photo-realistic videos from text on very challenging real-world video datasets.In addition, we construct a benchmark synthetic dataset of moving shapes to systematically evaluate our conditioning scheme.Extensive experiments demonstrate that TFGAN significantly outperforms the existing approaches, and can also generate videos of novel categories not seen during training.

用于从文本中生成视频的有效文本调节GAN框架

本文提出了一种基于GAN的视频生成方法，以文本描述为条件，采用一种新的调节方法，从编码的文本中生成卷积滤波器，并将其用于判别器的卷积。

本文提出了用于文本到视频合成的条件性GAN模型：开发文本特征条件的CNN过滤器，并构建了在视频/图像生成方面具有改进性能的移动形状数据集。


Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.However, compressive networks are desired in many real world applications and direct training of small networks may be trapped in local optima.In this paper, instead of pruning or distilling over-parameterized models to compressive ones, we propose a new approach based on \emph{differential inclusions of inverse scale spaces}, that generates a family of models from simple to complex ones by coupling gradient descent and mirror descent to explore model structural sparsity.It has a simple discretization, called the Split Linearized Bregman Iteration (SplitLBI), whose global convergence analysis in deep learning is established that from any initializations, algorithmic iterations converge to a critical point of empirical risks.Experimental evidence shows that\ SplitLBI may achieve state-of-the-art performance in large scale training on ImageNet-2012 dataset etc., while with \emph{early stopping} it unveils effective subnet architecture with comparable test accuracies to dense models after retraining instead of pruning well-trained ones.

SplitLBI被应用于深度学习，探索模型结构的稀疏性，在ImageNet-2012中取得了最先进的性能，并揭示了有效的子网结构。

提出了一种基于优化的算法，通过耦合权重矩阵的学习和稀疏性约束来寻找大规模神经网络的重要稀疏结构，在非凸优化问题上提供保证收敛性。


In this paper, we study the learned iterative shrinkage thresholding algorithm (LISTA) for solving sparse coding problems.  Following assumptions made by prior works, we first discover that the code components in its estimations may be lower than expected, i.e., require gains, and to address this problem, a gated mechanism amenable to theoretical analysis is then introduced.Specific design of the gates is inspired by convergence analyses of the mechanism and hence its effectiveness can be formally guaranteed.In addition to the gain gates, we further introduce overshoot gates for compensating insufficient step size in LISTA.Extensive empirical results confirm our theoretical findings and verify the effectiveness of our method.

我们提出了门控机制，以增强稀疏编码的学习型ISTA，并在理论上保证该方法的优越性。

提出了LISTA的扩展，通过引入 "增益门 "和包括 "过冲门 "的动量来解决低估问题，显示了改进的收敛率。

本文主要是通过提出一个 "增益门控函数 "来解决使用LISTA型网络的稀疏编码问题，以缓解 "无误报 "假设的弱点。


The learning of hierarchical representations for image classification has experienced an impressive series of successes due in part to the availability of large-scale labeled data for training.On the other hand, the trained classifiers have traditionally been evaluated on a handful of test images, which are deemed to be extremely sparsely distributed in the space of all natural images.It is thus questionable whether recent performance improvements on the excessively re-used test sets generalize to real-world natural images with much richer content variations.In addition, studies on adversarial learning show that it is effortless to construct adversarial examples that fool nearly all image classifiers, adding more complications to relative performance comparison of existing models.This work presents an efficient framework for comparing image classifiers, which we name the MAximum Discrepancy (MAD) competition.Rather than comparing image classifiers on fixed test sets, we adaptively sample a test set from an arbitrarily large corpus of unlabeled images so as to maximize the discrepancies between the classifiers, measured by the distance over WordNet hierarchy.Human labeling on the resulting small and model-dependent image sets reveals the relative performance of the competing classifiers and provides useful insights on potential ways to improve them.We report the MAD competition results of eleven ImageNet classifiers while noting that the framework is readily extensible and cost-effective to add future classifiers into the competition.

我们提出了一个高效和适应性的框架，用于比较图像分类器，使分类器之间的差异最大化，而不是在固定的测试集上进行比较。

错误发现机制，通过对 "分歧最大 "的测试集进行采样来比较图像分类器，通过WordNet本体的语义感知距离来衡量分歧。


Robustness of neural networks has recently been highlighted by the adversarial examples, i.e., inputs added with well-designed  perturbations which are imperceptible to humans but can cause the network to give incorrect outputs.In this paper, we design a new CNN architecture that by itself has good robustness.We introduce a simple but powerful technique, Random Mask, to modify existing CNN structures.We show that CNN with Random Mask achieves state-of-the-art performance against black-box adversarial attacks without applying any adversarial training.We next investigate the adversarial examples which â€œfoolâ€ a CNN with Random Mask.Surprisingly, we find that these adversarial examples often â€œfoolâ€ humans as well.This raises fundamental questions on how to define adversarial examples and robustness properly.

我们提出了一种修改CNN结构的技术，以提高稳健性，同时保持较高的测试精度，并通过生成能够愚弄人类的对抗性例子，对当前对抗性例子的定义是否合适提出疑问。

本文提出了一种简单的技术来提高神经网络对黑箱攻击的稳健性。

作者提出了一种简单的方法来提高卷积神经网络对对抗性例子的稳健性，并取得了令人惊讶的好结果。


Supervised deep learning methods require cleanly labeled large-scale datasets, but collecting such data is difficult and sometimes impossible.There exist two popular frameworks to alleviate this problem: semi-supervised learning and robust learning to label noise.Although these frameworks relax the restriction of supervised learning, they are studied independently.Hence, the training scheme that is suitable when only small cleanly-labeled data are available remains unknown.In this study, we consider learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled, and the rest is corrupt.Under this framework, we compare recent algorithms for semi-supervised and robust learning.The results suggest that semi-supervised learning outperforms robust learning with noisy labels.We also propose a training strategy for mixing mixup techniques to learn from such bi-quality data effectively.

我们建议在一个共享的设置下，比较半监督和稳健学习对噪声标签的影响

作者提出了一种基于mixup的策略，用于在正式场合训练模型，其中包括半监督和稳健学习任务作为特殊情况。


Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent multi-dimensional, structured data such as images.The simplest solution to solve this computationally hard problem is to decompose it into independent layerwise subproblems.However, neuroscientific evidence would suggest inter-connecting these subproblems as in the Predictive Coding (PC) theory, which adds top-down connections between consecutive layers.In this study, a new model called Sparse Deep Predictive Coding (SDPC) is introduced to assess the impact of this inter-layer feedback connection.In particular, the SDPC is compared with a Hierarchical Lasso (Hi-La) network made out of a sequence of Lasso layers.A 2-layered SDPC and a Hi-La networks are trained on 3 different databases and with different sparsity parameters on each layer.First, we show that the overall prediction error generated by SDPC is lower thanks to the feedback mechanism as it transfers prediction error between layers.Second, we demonstrate that the inference stage of the SDPC is faster to converge than for the Hi-La model.Third, we show that the SDPC also accelerates the learning process.Finally, the qualitative analysis of both models dictionaries, supported by their activation probability, show that the SDPC features are more generic and informative.

本文通过实验证明了分层稀疏编码算法中自上而下连接的有益效果。

本文介绍了一项比较分层稀疏编码技术的研究，表明自上而下的项有利于减少预测误差，并且可以更快地学习。


Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings.Recent work has primarily focused on explaining models for tasks like image classification or visual question answering.  In this paper, we introduce an explanation approach for image similarity models, where a model's output is a score measuring the similarity of two inputs rather than a classification.  In this task, an explanation depends on both of the input images, so standard methods do not apply.We propose an explanation method that pairs a saliency map identifying important image regions with an attribute that best explains the match.  We find that our explanations provide additional information not typically captured by saliency maps alone, and can also improve performance on the classic task of attribute recognition.Our approach's ability to generalize is demonstrated on two datasets from diverse domains, Polyvore Outfits and Animals with Attributes 2.

解释图像相似性模型的预测的黑箱方法。

介绍了图像相似性模型的解释方法，该方法确定了对相似性分数有积极贡献的属性，并将其与生成的突出性地图配对。

本文提出了一种解释机制，将典型的显著图区域与属性一起配对，用于相似性匹配的深度神经网络。


Adversarial examples have been shown to be an effective way of assessing the robustness of neural sequence-to-sequence (seq2seq) models, by applying perturbations to the input of a model leading to large degradation in performance.However, these perturbations are only indicative of a weakness in the model if they do not change the semantics of the input in a way that would change the expected output.Using the example of machine translation (MT), we propose a new evaluation framework for adversarial attacks on seq2seq models taking meaning preservation into account and demonstrate that existing methods may not preserve meaning in general.Based on these findings, we propose new constraints for attacks on word-based MT systems and show, via human and automatic evaluation, that they produce more semantically similar adversarial inputs.Furthermore, we show that performing adversarial training with meaning-preserving attacks is beneficial to the model in terms of adversarial robustness without hurting test performance.

你应该如何评估对seq2seq的对抗性攻击

作者研究了产生对抗性例子的方法，表明用最符合引入的意义保留标准的攻击进行对抗性训练，可以提高对这种类型的攻击的稳健性，而在非对抗性设置中不会出现退化。

本文是关于在Seq2Seq模型背景下的意义保留的对抗性扰动。


We introduce a new normalization technique that exhibits the fast convergence properties of batch normalization using a transformation of layer weights instead of layer outputs.The proposed technique keeps the contribution of positive and negative weights to the layer output in equilibrium.We validate our method on a set of standard benchmarks including CIFAR-10/100, SVHN and ILSVRC 2012 ImageNet.

批量规范化的另一种规范化技术

引入规范化技术，将卷积层的权重规范化。

本稿介绍了一种新的层间转换，即EquiNorm，以改进批处理规范化，不修改层的输入，而是修改层的权重。


We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a fixed length vector.In particular, this distribution is supported over the contexts which co-occur with the entity and are embedded in a suitable low-dimensional space.This enables us to consider the problem of representation learning with a perspective from Optimal Transport and take advantage of its numerous tools such as Wasserstein distance and Wasserstein barycenters.We elaborate how the method can be applied for obtaining unsupervised representations of text and illustrate the performance quantitatively as well as qualitatively on tasks such as measuring sentence similarity and word entailment, where we empirically observe significant gains (e.g., 4.1% relative improvement over Sent2vec and GenSen).The key benefits of the proposed approach include:(a) capturing uncertainty and polysemy via modeling the entities as distributions,(b) utilizing the underlying geometry of the particular task (with the ground cost),(c) simultaneously providing interpretability with the notion of optimal transport between contexts and(d) easy applicability on top of existing point embedding methods.In essence, the framework can be useful for any unsupervised or supervised problem (on text or other modalities); and only requires a co-occurrence structure inherent to many problems.The code, as well as pre-built histograms, are available under https://github.com/context-mover.

将每个实体表示为嵌入地面空间中的背景的概率分布。

建议从背景词的直方图中构建词的嵌入，而不是作为点向量，这样可以通过一种方法来测量两个词之间的距离，即直方图之间的最佳传输，这种方法将实体的表示从标准的 "向量空间中的点 "增加到具有位于该向量空间中某些点的直方图。


Over the last few years, the phenomenon of adversarial examples --- maliciously constructed inputs that fool trained machine learning models --- has captured the attention of the research community, especially when the adversary is restricted to making small modifications of a correctly handled input.At the same time, less surprisingly, image classifiers lack human-level performance on randomly corrupted images, such as images with additive Gaussian noise.In this work, we show that these are two manifestations of the same underlying phenomenon.We establish this connection in several ways.First, we find that adversarial examples exist at the same distance scales we would expect from a linear model with the same performance on corrupted images.Next, we show that Gaussian data augmentation during training improves robustness to small adversarial perturbations and that adversarial training improves robustness to several types of image corruptions.Finally, we present a model-independent upper bound on the distance from a corrupted image to its nearest error given test performance and show that in practice we already come close to achieving the bound, so that improving robustness further for the corrupted image distribution requires significantly reducing test error.All of this suggests that improving adversarial robustness should go hand in hand with improving performance in the presence of more general and realistic image corruptions.This yields a computationally tractable evaluation metric for defenses to consider: test error in noisy image distributions.

鉴于观察到的自然数据分布之外的模型的错误率，小的对抗性扰动应该是可以预期的。

本文通过考虑以每个测试点为中心的高斯分布中的 "错误率"，对高维空间中的对抗性实例提出了另一种观点。


Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training.Due to the cost of applying such models to down-stream tasks, several model compression techniques on pre-trained language representations have been proposed (Sun et al., 2019; Sanh, 2019).However, surprisingly,  the simple baseline of just pre-training and fine-tuning compact models has been overlooked.In this paper, we first show that pre-training remains important in the context of smaller architectures, and fine-tuning pre-trained compact models can be competitive to more elaborate methods proposed in concurrent work.Starting with pre-trained compact models, we then explore transferring task knowledge from large fine-tuned models through standard knowledge distillation.The resulting simple, yet effective and general algorithm, Pre-trained Distillation, brings further improvements.Through extensive experiments, we more generally explore the interaction between pre-training and distillation under two variables that have been under-studied: model size and properties of unlabeled task data.One surprising observation is that they have a compound effect even when sequentially applied on the same data.To accelerate future research, we will make our 24 pre-trained miniature BERT models publicly available.

研究自我监督学习和知识蒸馏在建立紧凑模型的背景下如何互动。

研究通过蒸馏法训练紧凑的预训练语言模型，并表明使用教师来蒸馏紧凑的学生模型比直接预训练模型表现得更好。

这份材料表明，直接对学生进行掩饰性语言建模的预训练比蒸馏要好，最好的是将两者结合起来，从这个预训练的学生模型中进行蒸馏。


In this paper, we investigate lossy compression of deep neural networks (DNNs) by weight quantization and lossless source coding for memory-efficient deployment.Whereas the previous work addressed non-universal scalar quantization and entropy coding of DNN weights, we for the first time introduce universal DNN compression by universal vector quantization and universal source coding.In particular, we examine universal randomized lattice quantization of DNNs, which randomizes DNN weights by uniform random dithering before lattice quantization and can perform near-optimally on any source without relying on knowledge of its probability distribution.Moreover, we present a method of fine-tuning vector quantized DNNs to recover the performance loss after quantization.Our experimental results show that the proposed universal DNN compression scheme compresses the 32-layer ResNet (trained on CIFAR-10) and the AlexNet (trained on ImageNet) with compression ratios of $47.1$ and $42.5$, respectively.

我们介绍了通用的深度神经网络压缩方案，它普遍适用于任何模型的压缩，无论它们的权重分布如何，都能发挥接近最佳的效果。

介绍了一个类似于深度压缩的网络压缩管道，使用随机晶格量化代替经典的向量化，并使用通用源编码（bzip2）代替Huffman编码。


What would be learned by variational autoencoder(VAE) and what influence the disentanglement of VAE?This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues theoretically in the idealistic situation and implementation issue practically through noise modeling perspective in the realistic case.  On intrinsic dimension issue, due to information conservation, the idealistic VAE learns and only learns intrinsic factor dimension.Besides, suggested by mutual information separation property, the constraint induced by Gaussian prior to the VAE objective encourages the information sparsity in dimension.On disentanglement issue,   subsequently, inspired by information conservation theorem the clarification on disentanglement in this paper is made.On real factor issue, due to factor equivalence, the idealistic VAE possibly learns any factor set in the equivalence class.  On indicator issue, the behavior of current disentanglement metric is discussed, and several performance indicators regarding the disentanglement and generating influence are subsequently raised to evaluate the performance of VAE model and to supervise the used factors.On implementation issue, the experiments under noise modeling and constraints empirically testify the theoretical analysis and also show their own characteristic in pursuing disentanglement.

本文试图从理论上初步解决理想情况下的脱钩问题，并在现实情况下通过噪声建模的角度进行实践。

研究了高斯VAE中噪声建模的重要性，并提出使用类似经验-贝叶斯的方式来训练噪声。

修改在开发VAE模型时对噪声因素的处理方式


Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of $L_2$ regularization.Literal weight decay has been shown to outperform $L_2$ regularization for optimizers for which they differ. We empirically investigate weight decay for three optimization algorithms (SGD, Adam, and K-FAC) and a variety of network architectures.We identify three distinct mechanisms by which weight decay exerts a regularization effect, depending on the particular optimization algorithm and architecture: (1) increasing the effective learning rate, (2) approximately regularizing the input-output Jacobian norm, and (3) reducing the effective damping coefficient for second-order optimization. Our results provide insight into how to improve the regularization of neural networks.

我们研究了不同优化器的权重衰减正则化，并确定了权重衰减改善泛化的三种不同机制。

讨论了权重衰减对深度网络模型训练的影响，包括有无批量规范化以及使用一/二阶优化方法时，并假设较大的学习率具有规范化效应。


In this paper we present the first freely available dataset for the development and evaluation of domain adaptation methods, for the sound event detection task.The dataset contains 40 log mel-band energies extracted from $100$ different synthetic sound event tracks, with additive noise from nine different acoustic scenes (from indoor, outdoor, and vehicle environments), mixed at six different sound-to-noise ratios, SNRs, (from -12 to -27 dB with a step of -3 dB), and totaling to 5400 (9 * 100 * 6) sound files and a total length of 30 564 minutes.We provide the dataset as is, the code to re-create the dataset and remix the sound event tracks and the acoustic scenes with different SNRs, and a baseline method that tests the adaptation performance with the proposed dataset and establishes some first results.

这是第一个免费提供的用于声音事件检测的领域适应性数据集。


This paper aims to address the limitations of mutual information estimators based on variational optimization.By redefining the cost using generalized functions from nonextensive statistical mechanics we raise the upper bound of previous estimators and enable the control of the bias variance trade off.Variational based estimators outperform previous methods especially in high dependence high dimensional scenarios found in machine learning setups.Despite their performance, these estimators either exhibit a high variance or are upper bounded by log(batch size).Our approach inspired by nonextensive statistical mechanics uses different generalizations for the logarithm and the exponential in the partition function.This enables the estimator to capture changes in mutual information over a wider range of dimensions and correlations of the input variables whereas previous estimators saturate them.

基于非广义统计力学的相互信息估计器

本文试图通过引入参数q和定义q代数来建立新的相互信息的变分下限，表明该下限具有较小的方差，并实现了高值。


Generative adversarial networks (GANs) are a widely used framework for learning generative models.Wasserstein GANs (WGANs), one of the most successful variants of GANs, require solving a minmax problem to global optimality, but in practice, are successfully trained with stochastic gradient descent-ascent.In this paper, we show that, when the generator is a one-layer network, stochastic gradient descent-ascent converges to a global solution in polynomial time and sample complexity.

我们表明，随机梯度下降上升法对具有单层生成器网络的WGAN来说收敛到了全局最优。

试图证明随机梯度下降-上升法可以收敛到WGAN的最小-最大问题的全局解。


Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space.While adversarial training improves the robustness of classifiers against such adversarial perturbations, it leaves classifiers sensitive to them on a non-negligible fraction of the inputs.We argue that there are two different kinds of adversarial perturbations: shared perturbations which fool a classifier on many inputs and singular perturbations which only fool the classifier on a small fraction of the data.We find that adversarial training increases the robustness of classifiers against shared perturbations.Moreover, it is particularly effective in removing universal perturbations, which can be seen as an extreme form of shared perturbations.Unfortunately, adversarial training does not consistently increase the robustness against singular perturbations on unseen inputs.However, we find that adversarial training decreases robustness of the remaining perturbations against image transformations such as changes to contrast and brightness or  Gaussian blurring.It thus makes successful attacks on the classifier in the physical world less likely.Finally, we show that even singular perturbations can be easily detected and must thus exhibit generalizable patterns even though the perturbations are specific for certain inputs.

我们的经验表明，对抗性训练能有效地消除普遍的扰动，使对抗性例子对图像变换的稳健性降低，并使其能被检测到的方法。

分析了对抗性训练及其对通用对抗性例子以及标准（基本迭代）对抗性例子的影响，以及对抗性训练如何影响检测。

作者表明，对抗性训练在防止 "共享 "对抗性扰动，特别是防止普遍扰动方面是有效的，但在防止单一扰动方面则不太有效。


We address the challenging problem of efficient deep learning model deployment, where the goal is to design neural network architectures that can fit different hardware platform constraints.Most of the traditional approaches either manually design or use Neural Architecture Search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally expensive and unscalable.Our key idea is to decouple model training from architecture search to save the cost.To this end, we propose to train a once-for-all network (OFA) that supports diverse architectural settings (depth, width, kernel size, and resolution).Given a deployment scenario, we can then quickly get a specialized sub-network by selecting from the OFA network without additional training.To prevent interference between many sub-networks during training, we also propose a novel progressive shrinking algorithm, which can train a surprisingly large number of sub-networks ($> 10^{19}$) simultaneously.Extensive experiments on various hardware platforms (CPU, GPU, mCPU, mGPU, FPGA accelerator) show that OFA consistently outperforms SOTA NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3) while reducing orders of magnitude GPU hours and $CO_2$ emission.In particular, OFA achieves a new SOTA 80.0% ImageNet top1 accuracy under the mobile setting ($<$600M FLOPs).Code and pre-trained models are released at https://github.com/mit-han-lab/once-for-all.

我们介绍了训练一个适合许多硬件平台的单一的一次性网络的技术。

该方法产生了一个网络，人们可以从该网络中提取各种资源限制（延迟、内存）的子网络，这些网络表现良好，不需要重新训练。

本文试图用一种基于预测的NAS方法来解决为专门的资源约束的部署场景寻找最佳架构的问题。


A deep generative model is a powerful method of learning a data distribution, which has achieved tremendous success in numerous scenarios.However, it is nontrivial for a single generative model to faithfully capture the distributions of the complex data such as images with complicate structures.In this paper, we propose a novel approach of cascaded boosting for boosting generative models, where meta-models (i.e., weak learners) are cascaded together to produce a stronger model.Any hidden variable meta-model can be leveraged as long as it can support the likelihood evaluation.We derive a decomposable variational lower bound of the boosted model, which allows each meta-model to be trained separately and greedily.We can further improve the learning power of the generative models by combing our cascaded boosting framework with the multiplicative boosting framework.

提出一种通过级联隐性变量模型提升生成模型的方法

本文提出了一种用于提升生成模型的级联提升的新方法，它允许每一个元模型被单独和贪婪地训练。


Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks.Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline.We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena.We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline.

我们在ELMo和相关的语境嵌入模型中探究句子结构。我们发现现有的模型能有效地编码句法，并显示出长程依赖的证据，但在语义任务上只提供小的改进。

提出了 "边缘探测 "方法，并关注跨度之间的关系，而不是单个词，使作者能够研究句法构成、依赖关系、实体标签和语义角色标签。

通过编译一套 "边缘探测 "任务，对什么是捕获的上下文嵌入词提供了新的见解。


Deep reinforcement learning has succeeded in sophisticated games such as Atari, Go, etc.Real-world decision making, however, often requires reasoning with partial information extracted from complex visual observations.This paper presents  Discriminative Particle Filter Reinforcement Learning (DPFRL), a new reinforcement learning framework for partial and complex observations.DPFRL encodes a differentiable particle filter with learned transition and observation models in a neural network, which allows for reasoning with partial observations over multiple time steps.While a standard particle filter relies on a generative observation model, DPFRL learns a discriminatively parameterized model that is training directly for decision making.We show that the discriminative parameterization results in significantly improved performance, especially for tasks with complex visual observations, because it circumvents the difficulty of modelling observations explicitly.In most cases, DPFRL outperforms state-of-the-art POMDP RL models in Flickering Atari Games, an existing POMDP RL benchmark, and in Natural Flickering Atari Games, a new, more challenging POMDP RL benchmark that we introduce.We further show that DPFRL performs well for visual navigation with real-world data.

我们介绍了DPFRL，这是一个在部分和复杂观察下的强化学习框架，具有完全可区分的颗粒过滤器。

介绍了训练具有潜在状态变量的DLR代理的想法，其模型为信念分布，因此它们可以处理部分观察的环境。

本文介绍了一种用于POMDP RL的原则性方法：判别性粒子过滤器强化学习，该方法允许在多个时间步骤中对部分观测进行推理，在基准上达到最先进的水平。


Extending models with auxiliary latent variables is a well-known technique to in-crease model expressivity.Bachman & Precup (2015); Naesseth et al. (2018); Cremer et al. (2017); Domke & Sheldon (2018) show that Importance Weighted Autoencoders (IWAE) (Burda et al., 2015) can be viewed as extending the variational family with auxiliary latent variables.Similarly, we show that this view encompasses many of the recent developments in variational bounds (Maddisonet al., 2017; Naesseth et al., 2018; Le et al., 2017; Yin & Zhou, 2018; Molchanovet al., 2018; Sobolev & Vetrov, 2018).The success of enriching the variational family with auxiliary latent variables motivates applying the same techniques to the generative model.We develop a generative model analogous to the IWAE bound and empirically show that it outperforms the recently proposed Learned Accept/Reject Sampling algorithm (Bauer & Mnih, 2018), while being substantially easier to implement.Furthermore, we show that this generative process provides new insights on ranking Noise Contrastive Estimation (Jozefowicz et al.,2016; Ma & Collins, 2018) and Contrastive Predictive Coding (Oord et al., 2018).

使用辅助变量变分推理对蒙特卡洛目标进行了分析，得出了CPC和NCE的新分析以及新的生成模型。

提出了关于用辅助潜变量模型改进变分界线的不同观点，并探讨了这些模型在生成模型中的使用。


Stochastic Gradient Descent or SGD is the most popular optimization algorithm for large-scale problems.SGD estimates the gradient by uniform sampling with sample size one.There have been several other works that suggest faster epoch wise convergence by using weighted non-uniform sampling for better gradient estimates.Unfortunately, the per-iteration cost of maintaining this adaptive distribution for gradient estimation is more than calculating the full gradient.As a result, the false impression of faster convergence in iterations leads to slower convergence in time, which we call a chicken-and-egg loop.In this paper, we break this barrier by providing the first demonstration of a sampling scheme, which leads to superior gradient estimation, while keeping the sampling cost per iteration similar to that of the uniform sampling.Such an algorithm is possible due to the sampling view of Locality Sensitive Hashing (LSH), which came to light recently.As a consequence of superior and fast estimation, we reduce the running time of all existing gradient descent algorithms.We demonstrate the benefits of our proposal on both SGD and AdaGrad.

我们改进了所有现有梯度下降算法的运行。 作者提出通过使用LSH从与梯度大小成比例的单调函数中抽样随机梯度。 考虑SGD对二次损失的例子之和形式的目标。


In recent years we have made significant progress identifying computational principles that underlie neural function.While not yet complete, we have sufficient evidence that a synthesis of these ideas could result in an understanding of how neural computation emerges from a combination of innate dynamics and plasticity, and which could potentially be used to construct new AI technologies with unique capabilities.I discuss the relevant principles, the advantages they have for computation, and how they can benefit AI.Limitations of current AI are generally recognized, but fewer people are aware that we understand enough about the brain to immediately offer novel AI formulations.

人们普遍认识到目前人工智能的局限性，但较少有人意识到我们对大脑的了解足以立即提供新颖的人工智能方案。


Recent work has demonstrated how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments.We propose question-answering as a general paradigm to decode and understand the representations that such agents develop, applying our method to two recent approaches to predictive modeling â€“ action-conditional CPC (Guo et al., 2018) and SimCore (Gregor et al., 2019).After training agents with these predictive objectives in a visually-rich, 3D environment with an assortment of objects, colors, shapes, and spatial configurations, we probe their internal state representations with a host of synthetic (English) questions, without backpropagating gradients from the question-answering decoder into the agent.The performance of different agents when probed in this way reveals that they learn to encode detailed, and seemingly compositional, information about objects, properties and spatial relations from their physical environment.Our approach is intuitive, i.e. humans can easily interpret the responses of the model as opposed to inspecting continuous vectors, and model-agnostic, i.e. applicable to any modeling approach.By revealing the implicit knowledge of objects, quantities, properties and relations acquired by agents as they learn, question-conditional agent probing can stimulate the design and development of stronger predictive learning objectives.

我们用回答问题的方式来评估代理能通过自我监督的预测学习多少关于环境的知识。

提出QA作为研究代理在世界中学习什么的工具，认为这对人类来说是一种直观的方法，允许有任意的复杂性。

作者提出了一个框架来评估由预测模型建立的表征，这些表征包含足够的信息来回答关于它们被训练的环境的问题，显示那些由SimCore建立的表征包含足够的信息让LSTM准确回答问题。


In most real-world scenarios, training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion.In this paper, we explore a novel yet simple way to alleviate this issue via synthesizing less-frequent classes with adversarial examples of other classes.Surprisingly, we found this counter-intuitive method can effectively learn generalizable features of minority classes by transferring and leveraging the diversity of the majority information.Our experimental results on various types of class-imbalanced datasets in image classification and natural language processing show that the proposed method not only improves the generalization of minority classes significantly compared to other re-sampling or re-weighting methods, but also surpasses other methods of state-of-art level for the class-imbalanced classification.

我们开发了一种利用对抗性例子进行不平衡分类的新方法

提出了一个新的优化目标，通过过度取样多数类而不是少数类来生成合成样本，解决了对少数类过度拟合的问题。

作者提出用重新抽样的方法来解决不平衡分类的问题，表明少数类中的对抗性例子将有助于训练出一个更好地泛化的新模型。


Active matter consists of active agents which transform energy extracted from surroundings into momentum, producing a variety of collective phenomena.A model, synthetic active system composed of microtubule polymers driven by protein motors spontaneously forms a liquid-crystalline nematic phase.Extensile stress created by the protein motors precipitates continuous buckling and folding of the microtubules creating motile topological defects and turbulent fluid flows.Defect motion is determined by the rheological properties of the material; however, these remain largely unquantified.Measuring defects dynamics can yield fundamental insights into active nematics, a class of materials that include bacterial films and animal cells.Current methods for defect detection lack robustness and precision, and require fine-tuning for datasets with different visual quality.  In this study, we applied Deep Learning to train a defect detector to automatically analyze microscopy videos of the microtubule active nematic.  Experimental results indicate that our method is robust and accurate.It is expected to significantly increase the amount of video data that can be processed.

CNN在软凝聚态物理实验中的一个有趣的应用。 作者证明，深度学习方法可以提高识别精度和识别向列液晶缺陷的速率。 应用一个众所周知的神经模型（YOLO）来检测图像中物体的边界盒。


In this work we study locality and compositionality in the context of learning representations for Zero Shot Learning (ZSL). In order to well-isolate the importance of these properties in learned representations, we impose the additional constraint that, differently from most recent work in ZSL, no pre-training on different datasets (e.g. ImageNet) is performed.The results of our experiment show how locality, in terms of small parts of the input, and compositionality, i.e. how well can the learned representations be expressed as a function of a smaller vocabulary, are both deeply related to generalization and motivate the focus on more local-aware models in future research directions for representation learning.

构成性和位置性对零样本学习的表征学习的影响分析。

提出了ZSL的评估框架，其中不允许对模型进行预训练，而是随机初始化模型参数，以便更好地了解ZSL中发生的情况。


It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples.In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear.Here we show that distributions of logit differences have a universal functional form.This functional form is independent of architecture, dataset, and training protocol; nor does it change during training.This leads to adversarial error having a universal scaling, as a power-law, with respect to the size of the adversarial perturbation.We show that this universality holds for a broad range of datasets (MNIST, CIFAR10, ImageNet, and random data), models (including state-of-the-art deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD).Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness.Finally, we study the effect of network architectures on adversarial sensitivity.To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR10.Our resulting architecture is more robust to white \emph{and} black box attacks compared to previous attempts.

在所有研究的数据集和模型中，对抗性错误具有类似的幂律形式，而且架构很重要。


Reinforcement learning (RL) has led to increasingly complex looking behavior in recent years.However, such complexity can be misleading and hides over-fitting.We find that visual representations may be a useful metric of complexity, and both correlates well objective optimization and causally effects reward optimization.We then propose curious representation learning (CRL) which allows us to use better visual representation learning algorithms to correspondingly increase visual representation in policy through an intrinsic objective on both simulated environments and transfer to real images.Finally, we show better visual representations induced by CRL allows us to obtain better performance on Atari without any reward than other curiosity objectives.

我们提出了一个将好奇心作为视觉表征学习问题的表述，并表明它可以在代理中实现良好的视觉表征。

本文将基于好奇心的RL训练表述为学习一个视觉表征模型，认为专注于更好的LR和最大化新奇场景的模型损失将得到更好的整体性能。


This paper introduces the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances comprising the scene and infer their complete object geometry.This enables a semantically meaningful decomposition of a scanned scene into individual, complete 3D objects, including hidden and unobserved object parts.This will open up new possibilities for interactions with object in a scene, for instance for virtual or robotic agents.To address this task, we propose 3D-SIC, a new data-driven approach that jointly detects object instances and predicts their completed geometry.The core idea of 3D-SIC is a novel end-to-end 3D neural network architecture that leverages joint color and geometry feature learning.The fully-convolutional nature of our 3D network enables efficient inference of semantic instance completion for 3D scans at scale of large indoor environments in a single forward pass.In a series evaluation, we evaluate on both real and synthetic scan benchmark data, where we outperform state-of-the-art approaches by over 15 in mAP@0.5 on ScanNet, and over 18 in mAP@0.5 on SUNCG.

从一个场景的不完整的RGB-D扫描中，我们的目标是检测构成该场景的各个物体实例，并推断出它们完整的物体几何形状。

提出了一个端到端的3D CNN结构，它结合了颜色特征和3D特征，从RGB-D扫描中预测场景中缺失的3D结构。

作者提出了一个新颖的端到端三维卷积网络，它可以预测三维语义实例的完成情况，作为对象的边界框、类标签和完整的物体几何。


Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter.Here we tackle the more generic problem of semantic style transfer: given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains.We introduce XGAN ("Cross-GAN"), a dual adversarial autoencoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions.  We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the model to preserve semantics in the learned embedding space.We report promising qualitative results for the task of face-to-cartoon translation.The cartoon dataset we collected for this purpose will also be released as a new benchmark for semantic style transfer.

XGAN是一个无监督的模型，用于特征级的图像到图像的转换，适用于语义风格的转移问题，如脸部到卡通任务，为此我们引入了一个新的数据集。

本文提出了一个新的基于GAN的模型，用于类似DTN非配对图像间的转换


Training neural networks on large datasets can be accelerated by distributing the workload over a network of machines.As datasets grow ever larger, networks of hundreds or thousands of machines become economically viable.The time cost of communicating gradients limits the effectiveness of using such large machine counts, as may the increased chance of network faults.We explore a particularly simple algorithm for robust, communication-efficient learning---signSGD.Workers transmit only the sign of their gradient vector to a server, and the overall update is decided by a majority vote.This algorithm uses 32x less communication per iteration than full-precision, distributed SGD.Under natural conditions verified by experiment, we prove that signSGD converges in the large and mini-batch settings, establishing convergence for a parameter regime of Adam as a byproduct.Aggregating sign gradients by majority vote means that no individual worker has too much power.We prove that unlike SGD, majority vote is robust when up to 50% of workers behave adversarially.The class of adversaries we consider includes as special cases those that invert or randomise their gradient estimate.On the practical side, we built our distributed training system in Pytorch.Benchmarking against the state of the art collective communications library (NCCL), our framework---with the parameter server housed entirely on one machine---led to a 25% reduction in time for training resnet50 on Imagenet when using 15 AWS p3.2xlarge machines.

工作者向服务器发送梯度标志，更新由多数人投票决定。我们表明，这种算法在理论上和实践上都是收敛的，通信效率高且容错。

提出了一个以多数票为聚合的signSGD的分布式实现。


Profiling cellular phenotypes from microscopic imaging can provide meaningful biological information resulting from various factors affecting the cells.One motivating application is drug development: morphological cell features can be captured from images, from which similarities between different drugs applied at different dosages can be quantified.The general approach is to find a function mapping the images to an embedding space of manageable dimensionality whose geometry captures relevant features of the input images.An important known issue for such methods is separating relevant biological signal from nuisance variation.For example, the embedding vectors tend to be more correlated for cells that were cultured and imaged during the same week than for cells from a different week, despite having identical drug compounds applied in both cases.In this case, the particular batch a set of experiments were conducted in constitutes the domain of the data; an ideal set of image embeddings should contain only the relevant biological information (e.g. drug effects).We develop a general framework for adjusting the image embeddings in order to `forget' domain-specific information while preserving relevant biological information.To do this, we minimize a loss function based on distances between marginal distributions (such as the Wasserstein distance) of embeddings across domains for each replicated treatment.For the dataset presented, the replicated treatment is the negative control.We find that for our transformed embeddings (1) the underlying geometric structure is not only preserved but the embeddings also carry improved biological signal (2) less domain-specific information is present.

我们纠正不同领域的图像嵌入的干扰性变化，只保留相关信息。

讨论了一种调整图像嵌入的方法，以便将技术变化与生物信号区分开来。

作者提出了一种方法，通过训练一个最小化分布之间的Wasserstein距离的网络，在保留相关生物信息的同时去除特定领域信息。


This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size.MINE is  back-propable and we prove that it is strongly consistent.We illustrate a handful of applications in which MINE is succesfully applied  to enhance the property of generative models in both unsupervised and supervised settings.We apply our framework to estimate the information bottleneck, and apply it in tasks related to supervised classification problems.Our results  demonstrate substantial added flexibility and improvement in these settings.

一个在样本量和维度上可扩展的相互信息估计器。


Reinforcement learning methods have recently achieved impressive results on a wide range of control problems.However, especially with complex inputs, they still require an extensive amount of training data in order to converge to a meaningful solution.This limitation largely prohibits  their usage for complex input spaces such as video signals, and it is still impossible to use it for a number of complex problems in a real world environments, including many of those for video based control.Supervised learning, on the contrary, is capable of learning on a relatively small number of samples, however it does not take into account reward-based control policies and is not capable to provide independent control policies.  In this article we propose a model-free control method, which uses a combination of reinforcement and supervised learning for autonomous control and paves the way towards policy based control in real world environments.We use SpeedDreams/TORCS video game to demonstrate that our approach requires much less samples (hundreds of thousands against millions or tens of millions) comparing to the state-of-the-art reinforcement learning techniques on similar data, and at the same time overcomes both supervised and reinforcement learning approaches in terms of quality.Additionally, we demonstrate the applicability of the method to MuJoCo control problems.

强化学习和监督学习的新组合，极大地减少了视频训练所需的样本数量 本文提出利用标记的受控数据来加速基于强化的控制策略的学习。


A typical experiment to study cognitive function is to train animals to perform tasks, while the researcher records the electrical activity of the animals neurons.The main obstacle faced, when using this type of electrophysiological experiment to uncover the circuit mechanisms underlying complex behaviors, is our incomplete access to relevant circuits in the brain.One promising approach is to model neural circuits using an artificial neural network (ANN), which can provide complete access to the â€œneural circuitsâ€ responsible for a behavior.More recently, reinforcement learning models have been adopted to understand the functions of cortico-basal ganglia circuits as reward-based learning has been found in mammalian brain.In this paper, we propose a Biologically-plausible Actor-Critic with Episodic Memory (B-ACEM) framework to model a prefrontal cortex-basal ganglia-hippocampus (PFC-BG) circuit, which is verified to capture the behavioral findings from a well-known perceptual decision-making task, i.e., random dots motion discrimination.This B-ACEM framework links neural computation to behaviors, on which we can explore how episodic memory should be considered to govern future decision.Experiments are conducted using different settings of the episodic memory and results show that all patterns of episodic memories can speed up learning.In particular, salient events are prioritized to propagate reward information and guide decisions.Our B-ACEM framework and the built-on experiments give inspirations to both designs for more standard decision-making models in biological system and a more biologically-plausible ANN.

前额叶皮层-基底神经节-海马体（PFC-BG）回路的生物合理性框架验证了通过情景记忆的快速学习


Understanding the representational power of Deep Neural Networks (DNNs) and how their structural properties (e.g., depth, width, type of activation unit) affect the functions they can compute, has been an important yet challenging question in deep learning and approximation theory.In a seminal paper, Telgarsky high- lighted the benefits of depth by presenting a family of functions (based on sim- ple triangular waves) for which DNNs achieve zero classification error, whereas shallow networks with fewer than exponentially many nodes incur constant error.Even though Telgarskyâ€™s work reveals the limitations of shallow neural networks, it doesnâ€™t inform us on why these functions are difficult to represent and in fact he states it as a tantalizing open question to characterize those functions that cannot be well-approximated by smaller depths.In this work, we point to a new connection between DNNs expressivity and Sharkovskyâ€™s Theorem from dynamical systems, that enables us to characterize the depth-width trade-offs of ReLU networks for representing functions based on the presence of a generalized notion of fixed points, called periodic points (a fixed point is a point of period 1).Motivated by our observation that the triangle waves used in Telgarskyâ€™s work contain points of period 3 â€“ a period that is special in that it implies chaotic behaviour based on the celebrated result by Li-Yorke â€“ we proceed to give general lower bounds for the width needed to represent periodic functions as a function of the depth.Technically, the crux of our approach is based on an eigenvalue analysis of the dynamical systems associated with such functions.

在这项工作中，我们指出了DNN的表现力和来自动力系统的Sharkovsky定理之间的新联系，这使我们能够描述ReLU网络的深度-宽度权衡。

显示了NN的表达能力如何取决于它的深度和宽度，进一步理解了深度网络对表示某些函数类别的好处。

作者推导出了深度-宽度权衡条件，以确定relu网络何时能够使用动态系统分析来表示周期函数。


We investigate low-bit quantization to reduce computational cost of deep neural network (DNN) based keyword spotting (KWS).We propose approaches to further reduce quantization bits via integrating quantization into keyword spotting model training, which we refer to as quantization-aware training.Our experimental results on large dataset indicate that quantization-aware training can recover performance models quantized to lower bits representations.By combining quantization-aware training and weight matrix factorization, we are able to significantly reduce model size and computation for small-footprint keyword spotting, while maintaining performance.

我们研究了在非常低位的量化关键字探测仪中的量化感知训练，以降低设备上的关键字探测成本。

本报告提出了一种结合低秩分解和量化的方法来压缩用于关键词发现的DNN模型。


Single-cell RNA-sequencing (scRNA-seq) is a powerful tool for analyzing biological systems.However, due to biological and technical noise, quantifying the effects of multiple experimental conditions presents an analytical challenge.To overcome this challenge, we developed MELD: Manifold Enhancement of Latent Dimensions.MELD leverages tools from graph signal processing to learn a latent dimension within the data scoring the prototypicality of each datapoint with respect to experimental or control conditions.We call this dimension the Enhanced Experimental Signal (EES).MELD learns the EES by filtering the noisy categorical experimental label in the graph frequency domain to recover a smooth signal with continuous values.This method can be used to identify signature genes that vary between conditions and identify which cell types are most affected by a given perturbation.We demonstrate the advantages of MELD analysis in two biological datasets, including T-cell activation in response to antibody-coated beads and treatment of human pancreatic islet cells with interferon gamma.

用于量化单细胞生物医学数据中实验扰动影响的新型图形信号处理框架。

本文介绍了几种处理生物细胞实验结果的方法，并提出了一种MELD算法，将硬组分配映射为软分配，使相关的细胞组得以聚类。


Models of user behavior are critical inputs in many prescriptive settings and can be viewed as decision rules that transform state information available to the user into actions.Gaussian processes (GPs), as well as nonlinear extensions thereof, provide a flexible framework to learn user models in conjunction with approximate Bayesian inference.However, the resulting models may not be interpretable in general.We propose decision-rule GPs (DRGPs) that apply GPs in a transformed space defined by decision rules that have immediate interpretability to practitioners.We illustrate this modeling tool on a real application and show that structural variational inference techniques can be used with DRGPs.We find that DRGPs outperform the direct use of GPs in terms of out-of-sample performance.

我们提出了一类基于使用高斯过程的用户模型，适用于由决策规则定义的转换空间。


While Bayesian optimization (BO) has achieved great success in optimizing expensive-to-evaluate black-box functions, especially tuning hyperparameters of neural networks, methods such as random search (Li et al., 2016) and multi-fidelity BO (e.g. Klein et al. (2017)) that exploit cheap approximations, e.g. training on a smaller training data or with fewer iterations, can outperform standard BO approaches that use only full-fidelity observations.In this paper, we propose a novel Bayesian optimization algorithm, the continuous-fidelity knowledge gradient (cfKG) method, that can be used when fidelity is controlled by one or more continuous settings such as training data size and the number of training iterations.cfKG characterizes the value of the information gained by sampling a point at a given fidelity, choosing to sample at the point and fidelity with the largest value per unit cost.Furthermore, cfKG can be generalized, following Wu et al. (2017), to settings where derivatives are available in the optimization process, e.g. large-scale kernel learning, and where more than one point can be evaluated simultaneously.Numerical experiments show that cfKG outperforms state-of-art algorithms when optimizing synthetic functions, tuning convolutional neural networks (CNNs) on CIFAR-10 and SVHN, and in large-scale kernel learning.

我们提出了一种贝叶斯最优的贝叶斯优化算法，通过利用廉价的近似值来进行超参数的调整。

通过贝叶斯优化研究超参数优化，使用知识梯度框架，并允许贝叶斯优化器针对成本调整保真度。


Neural networks trained only to optimize for training accuracy can often be fooled by adversarial examples --- slightly perturbed inputs misclassified with high confidence.Verification of networks enables us to gauge their vulnerability to such adversarial examples.We formulate verification of piecewise-linear neural networks as a mixed integer program.On a representative task of finding minimum adversarial distortions, our verifier is two to three orders of magnitude quicker than the state-of-the-art.We achieve this computational speedup via tight formulations for non-linearities, as well as a novel presolve algorithm that makes full use of all information available.The computational speedup allows us to verify properties on convolutional and residual networks with over 100,000 ReLUs --- several orders of magnitude more than networks previously verified by any complete verifier.In particular, we determine for the first time the exact adversarial accuracy of an MNIST classifier to perturbations with bounded l-âˆž norm Îµ=0.1: for this classifier, we find an adversarial example for 4.38% of samples, and a certificate of robustness to norm-bounded perturbations for the remainder.Across all robust training procedures and network architectures considered, and for both the MNIST and CIFAR-10 datasets, we are able to certify more samples than the state-of-the-art and find more adversarial examples than a strong first-order attack.

我们用超过10万个ReLU有效地验证了深度神经模型的稳健性，比最先进的技术认证了更多的样本，并发现了比强一阶攻击更多的对抗性例子。

对用于验证神经网络对对抗性扰动的稳健性的混合整数线性规划方法进行了仔细研究，并提出了对神经网络验证的MILP公式的三个增强。


Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas.However, most state-of-the-art deep learning models either fail to obtain uncertainty estimation or need significant modification (e.g., formulating a proper Bayesian treatment) to obtain it.None of the previous methods are able to take an arbitrary model off the shelf and generate uncertainty estimation without retraining or redesigning it.To address this gap, we perform the first systematic exploration into training-free uncertainty estimation. We propose three simple and scalable methods to analyze the variance of output from a trained network under tolerable perturbations: infer-transformation, infer-noise, and infer-dropout.They operate solely during inference, without the need to re-train, re-design, or fine-tune the model, as typically required by other state-of-the-art uncertainty estimation methods.Surprisingly, even without involving such perturbations in training, our methods produce comparable or even better uncertainty estimation when compared to other training-required state-of-the-art methods.Last but not least, we demonstrate that the uncertainty from our proposed methods can be used to improve the neural network training.

一套方法用于获得任何给定模型的不确定性估计，而无需重新设计、重新训练或对其进行微调。

描述了在训练过程中没有失真的情况下，测量任意神经网络的不确定性的几种方法。


Capturing spatiotemporal dynamics is an essential topic in video recognition.In this paper, we present learnable higher-order operation as a generic family of building blocks for capturing higher-order correlations from high dimensional input video space.We prove that several successful architectures for visual classification tasks are in the family of higher-order neural networks, theoretical and experimental analysis demonstrates their underlying mechanism is higher-order.  On the task of video recognition, even using RGB only without fine-tuning with other video datasets, our higher-order models can achieve results on par with or better than the existing state-of-the-art methods on both Something-Something (V1 and V2) and Charades datasets.

为情境学习提出的高阶操作

提出了一个新的三维卷积块，它将视频输入与它的情境进行卷积，基于相关情境存在于图像对象周围的假设。


Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters.To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures.The consistency loss dramatically improves generalization performance over supervised-only training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data.Motivated by these observations, we propose to train consistency-based methods with Stochastic Weight Averaging (SWA), a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule.We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule.With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data.For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%.

基于一致性的半监督学习模型不会收敛到一个单一的点，而是继续在一个平坦区域的周边探索不同的合理解决方案。权重平均化有助于提高泛化性能。

本文提出将随机权重平均法应用于半监督学习背景下，认为半监督的MT/PI模型特别适合于SWA，并提出快速SWA以加快训练速度。


In this paper, we find that by designing a novel loss function entitled, ''tracking loss'', Convolutional Neural Network (CNN) based object detectors can be successfully converted to well-performed visual trackers without any extra computational cost.This property is preferable to visual tracking where annotated video sequences for training are always absent, because rich features learned by detectors from still images could be utilized by dynamic trackers.It also avoids extra machinery such as feature engineering and feature aggregation proposed in previous studies.Tracking loss achieves this property by exploiting the internal structure of feature maps within the detection network and treating different feature points discriminatively.Such structure allows us to simultaneously consider discrimination quality and bounding box accuracy which is found to be crucial to the success.We also propose a network compression method to accelerate tracking speed without performance reduction.That also verifies tracking loss will remain highly effective even if the network is drastically compressed.Furthermore, if we employ a carefully designed tracking loss ensemble, the tracker would be much more robust and accurate.Evaluation results show that our trackers (including the ensemble tracker and two baseline trackers), outperform all state-of-the-art methods on VOT 2016 Challenge in terms of Expected Average Overlap (EAO) and robustness.We will make the code publicly available.

从损失函数的角度看，我们成功地将一个流行的检测器RPN转换为一个表现良好的跟踪器。


We study the problem of semantic code repair, which can be broadly defined as automatically fixing non-syntactic bugs in source code.The majority of past work in semantic code repair assumed access to unit tests against which candidate repairs could be validated.In contrast, the goal here is to develop a strong statistical model to accurately predict both bug locations and exact fixes without access to information about the intended correct behavior of the program.Achieving such a goal requires a robust contextual repair model, which we train on a large corpus of real-world source code that has been augmented with synthetically injected bugs.Our framework adopts a two-stage approach where first a large set of repair candidates are generated by rule-based processors, and then these candidates are scored by a statistical model using a novel neural network architecture which we refer to as Share, Specialize, and Compete.Specifically, the architecture (1) generates a  shared encoding of the source code using an RNN over the abstract syntax tree, (2) scores each candidate repair using specialized network modules, and (3) then normalizes these scores together so they can compete against one another in comparable probability space.We evaluate our model on a real-world test set gathered from GitHub containing four common categories of bugs.Our model is able to predict the exact correct repair 41% of the time with a single guess, compared to 13% accuracy for an attentional sequence-to-sequence model.

一种用于对程序修复候选者进行评分和排名的神经结构，以便在不访问单元测试的情况下静态地进行语义程序修复。

提出了一个由共享、专门化和竞争部分组成的神经网络架构，用于修复四种情况下的代码。


Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019).Such a dilemma is shown to be rooted in the inherently higher sample complexity (Schmidt et al., 2018) and/or model capacity (Nakkiran, 2019), for learning a high-accuracy and robust classifier.In view of that, give a classification task, growing the model capacity appears to help draw a win-win between accuracy and robustness, yet at the expense of model size and latency, therefore posing challenges for resource-constrained applications.Is it possible to co-design model accuracy, robustness and efficiency to achieve their triple wins?This paper studies multi-exit networks associated with input-adaptive efficient inference, showing their strong promise in achieving a â€œsweet point" in co-optimizing model accuracy, robustness, and efficiency.Our proposed solution, dubbed Robust Dynamic Inference Networks (RDI-Nets), allows for each input (either clean or adversarial) to adaptively choose one of the multiple output layers (early branches or the final one) to output its prediction.That multi-loss adaptivity adds new variations and flexibility to adversarial attacks and defenses, on which we present a systematical investigation.We show experimentally that by equipping existing backbones with such robust adaptive inference, the resulting RDI-Nets can achieve better accuracy and robustness, yet with over 30% computational savings, compared to the defended original models.

是否有可能共同设计模型的准确性、稳健性和效率，以实现其三赢？是的!

利用输入适应性的多个早期出口，用于对抗性攻击和防御领域，在不与较大容量假设冲突的情况下，降低平均推理复杂度。


Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret.Especially, little is known about how they represent language in their intermediate layers.In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns.In order to quantitatively analyze such intriguing phenomenon, we propose a concept alignment method based on how units respond to replicated text.We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language.

我们表明，在NLP任务中学习的CNN表征中的单个单元对特定的自然语言概念有选择性的响应。

使用保留意义的自然语言的语法单位，表明在NLP任务中学习的深度CNN的单位可以作为自然语言概念检测器。


We study the problem of building models that disentangle independent factors of variation.Such models encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis.As data we use a weakly labeled training set, where labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown.This labeling is of particular interest as it may be readily available without annotation costs.We introduce an autoencoder model and train it through constraints on image pairs and triplets.We show the role of feature dimensionality and adversarial training theoretically and experimentally.We formally prove the existence of the reference ambiguity, which is inherently present in the disentangling task when weakly labeled data is used.The numerical value of a factor has different meaning in different reference frames.When the reference depends on other factors, transferring that factor becomes ambiguous.We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.

这主要是一篇理论性的论文，描述了使用自动编码器和GAN来分解变异因素的挑战。

本文考虑分解图像中的变异因素，表明在一般情况下，如果没有进一步的假设，人们无法区分两种不同的变异因素，并提出了一种新的AE+GAN架构来尝试分解变异因素。

本文研究了在弱标签数据下分解独立变异因素的挑战，并引入了数据点映射的参考模糊性一词。


In information retrieval, learning to rank constructs a machine-based ranking model which given a query, sorts the search results by their degree of relevance or importance to the query.Neural networks have been successfully applied to this problem, and in this paper, we propose an attention-based deep neural network which better incorporates different embeddings of the queries and search results with an attention-based mechanism.This model also applies a decoder mechanism to learn the ranks of the search results in a listwise fashion.The embeddings are trained with convolutional neural networks or the word2vec model.We demonstrate the performance of this model with image retrieval and text querying data sets.

学会用几种嵌入和注意力的方式进行排序 建议在学习排名任务中使用注意力来结合查询和搜索结果的多种输入表征。


Computational neuroscience aims to fit reliable models of in vivo neural activity and interpret them as abstract computations.Recent work has shown that functional diversity of neurons may be limited to that of relatively few cell types; other work has shown that incorporating constraints into artificial neural networks (ANNs) can improve their ability to mimic neural data.Here we develop an algorithm that takes as input recordings of neural activity and returns clusters of neurons by cell type and models of neural activity constrained by these clusters.The resulting models are both more predictive and more interpretable, revealing the contributions of functional cell types to neural computation and ultimately informing the design of future ANNs.

我们开发了一种算法，将神经活动的记录作为输入，并按细胞类型返回神经元的集群和受这些集群制约的神经活动的模型。


Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs.In almost all cases so far, however, they have been applied to directly recovering a final solution from raw inputs, without explicit guidance on how to structure their problem-solving.Here, instead, we focus on learning in the space of algorithms: we train several state-of-the-art GNN architectures to imitate individual steps of classical graph algorithms, parallel (breadth-first search, Bellman-Ford) as well as sequential (Prim's algorithm).As graph algorithms usually rely on making discrete decisions within neighbourhoods, we hypothesise that maximisation-based message passing neural networks are best-suited for such objectives, and validate this claim empirically.We also demonstrate how learning in the space of algorithms can yield new opportunities for positive transfer between tasks---showing how learning a shortest-path algorithm can be substantially improved when simultaneously learning a reachability algorithm.

我们监督图神经网络模仿经典图形算法的中间和阶梯式输出，恢复了非常有利的见解。 建议通过学习基元和子程序而不是最终的输出，来训练神经网络模仿图算法。


Prospection is an important part of how humans come up with new task plans, but has not been explored in depth in robotics.Predicting multiple task-level is a challenging problem that involves capturing both task semantics and continuous variability over the state of the world.Ideally, we would combine the ability of machine learning to leverage big data for learning the semantics of a task, while using techniques from task planning to reliably generalize to new environment.In this work, we propose a method for learning a model encoding just such a representation for task planning.We learn a neural net that encodes the k most likely outcomes from high level actions from a given world.Our approach creates comprehensible task plans that allow us to predict changes to the environment many time steps into the future.We demonstrate this approach via application to a stacking task in a cluttered environment, where the robot must select between different colored blocks while avoiding obstacles, in order to perform a task.We also show results on a simple navigation task.Our algorithm generates realistic image and pose predictions at multiple points in a given task.

我们描述了一个架构，用于在机器人操纵任务中为中间目标生成不同的假设。 评估了一个拟议的生成性预测模型的质量，以生成机器人执行的计划。 本文提出了一种学习高层次过渡函数的方法，对任务规划很有帮助。


Adaptive gradient algorithms perform gradient-based updates using the history of gradients and are ubiquitous in training deep neural networks.While adaptive gradient methods theory is well understood for minimization problems, the underlying factors driving their empirical success in min-max problems such as GANs remain unclear.In this paper, we aim at bridging  this gap from both theoretical and empirical perspectives.First, we analyze a variant of Optimistic Stochastic Gradient (OSG) proposed in~\citep{daskalakis2017training} for solving a class of non-convex non-concave min-max problem and establish $O(\epsilon^{-4})$ complexity for finding $\epsilon$-first-order stationary point, in which the algorithm only requires invoking one stochastic first-order oracle while enjoying state-of-the-art iteration complexity achieved by stochastic extragradient method by~\citep{iusem2017extragradient}.Then we propose an adaptive variant of OSG named Optimistic Adagrad (OAdagrad) and reveal an \emph{improved} adaptive complexity $\widetilde{O}\left(\epsilon^{-\frac{2}{1-\alpha}}\right)$~\footnote{Here $\widetilde{O}(\cdot)$ compresses a logarithmic factor of $\epsilon$.}, where $\alpha$ characterizes the growth rate of the cumulative stochastic gradient and $0\leq \alpha\leq 1/2$.To the best of our knowledge, this is the first work for establishing adaptive complexity in non-convex non-concave min-max optimization.Empirically, our experiments show that indeed adaptive gradient algorithms outperform their non-adaptive counterparts in GAN training.Moreover, this observation can be explained by the slow growth rate of the cumulative stochastic gradient, as observed empirically.

本文对自适应梯度算法作为GAN解决非凸非凹最小值问题进行了新的分析，并通过实证研究解释了自适应梯度方法优于其非自适应同行的原因。

开发了在随机环境下解决变分不等式的算法，提出了外梯度方法的变体。


We consider the problem of unsupervised learning of a low dimensional, interpretable, latent state of a video containing a moving object.The problem of distilling dynamics from pixels has been extensively considered through the lens of graphical/state space models that exploit Markov structure for cheap computation and structured graphical model priors for enforcing interpretability on latent representations.We take a step towards extending these approaches by discarding the Markov structure; instead, repurposing the recently proposed Gaussian Process Prior Variational Autoencoder for learning sophisticated latent trajectories.We describe the model and perform experiments on a synthetic dataset and see that the model reliably reconstructs smooth dynamics exhibiting U-turns and loops.We also observe that this model may be trained without any beta-annealing or freeze-thaw of training parameters.Training is performed purely end-to-end on the unmodified evidence lower bound objective.This is in contrast to previous works, albeit for slightly different use cases, where application specific training tricks are often required.

我们通过使用带有高斯过程先验的VAE结构，只从一个玩具视频数据集的像素中学习物体的精确轨迹。


Dreams and our ability to recall them are among the most puzzling questions in sleep research.Specifically, putative differences in brain network dynamics between individuals with high versus low dream recall rates, are still poorly understood.In this study, we addressed this question as a classification problem where we applied deep convolutional networks (CNN) to sleep EEG recordings to predict whether subjects belonged to the high or low dream recall group (HDR and LDR resp.).Our model achieves significant accuracy levels across all the sleep stages, thereby indicating subtle signatures of dream recall in the sleep microstructure.We also visualized the feature space to inspect the subject-specificity of the learned features, thus ensuring that the network captured population level differences.Beyond being the first study to apply deep learning to sleep EEG in order to classify HDR and LDR, guided backpropagation allowed us to visualize the most discriminant features in each sleep stage.The significance of these findings and future directions are discussed.

我们使用卷积神经网络和特征可视化技术，如tSNE和引导-反向传播，研究梦境回忆的神经基础。


This paper considers multi-agent reinforcement learning (MARL) in networked system control.Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors.We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to stabilize the training of each local agent.Further, we propose a new differentiable communication protocol, called NeurComm, to reduce information loss and non-stationarity in NMARL.Based on experiments in realistic NMARL scenarios of adaptive traffic signal control and cooperative adaptive cruise control, an appropriate spatial discount factor effectively enhances the learning curves of non-communicative MARL algorithms, while NeurComm outperforms existing communication protocols in both learning efficiency and control performance.

本文提出了网络化多代理控制问题的新公式和新通信协议

关注N-MARL's，其中代理只根据来自邻近节点的消息更新他们的策略，表明引入空间折扣因素可以稳定学习。


Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights.Recent work developing this class of methods has explored ever richer parameterizations of the approximate posterior in the hope of improving performance.In contrast, here we share a curious experimental finding that suggests instead restricting the variational distribution to a more compact parameterization.For a variety of deep Bayesian neural networks trained using Gaussian mean-field variational inference, we find that the posterior standard deviations consistently exhibit strong low-rank structure after convergence.This means that by decomposing these variational parameters into a low-rank factorization, we can make our variational approximation more compact without decreasing the models' performance.Furthermore, we find that such factorized parameterizations improve the signal-to-noise ratio of stochastic gradient estimates of the variational lower bound, resulting in faster convergence.

均值字段VB使用了两倍的参数；我们在均值字段VB中绑定了方差参数，而在ELBO中没有任何损失，获得了速度和更低的方差梯度。


Aspect extraction in online product reviews is a key task in sentiment analysis and opinion mining.Training supervised neural networks for aspect extraction is not possible when ground truth aspect labels are not available, while the unsupervised neural topic models fail to capture the particular aspects of interest.In this work, we propose a weakly supervised approach for training neural networks for aspect extraction in cases where only a small set of seed words, i.e., keywords that describe an aspect, are available.Our main contributions are as follows.First, we show that current weakly supervised networks fail to leverage the predictive power of the available seed words by comparing them to a simple bag-of-words classifier.  Second, we propose a distillation approach for aspect extraction where the seed words are considered by the bag-of-words classifier (teacher) and distilled to the parameters of a neural network (student).Third, we show that regularization encourages the student to consider non-seed words for classification and, as a result, the student outperforms the teacher, which only considers the seed words.Finally, we empirically show that our proposed distillation approach outperforms (by up to 34.4% in F1 score) previous weakly supervised approaches for aspect extraction in six domains of Amazon product reviews.

我们有效地利用一些关键词作为弱监督来训练神经网络的方面提取。

讨论了一种知识蒸馏的变体，它使用基于种子词的词包分类器的 "教师 "和基于嵌入的神经网络的 "学生"。


Forming perceptual groups and individuating objects in visual scenes is an essential step towards visual intelligence.This ability is thought to arise in the brain from computations implemented by bottom-up, horizontal, and top-down connections between neurons.However, the relative contributions of these connections to perceptual grouping are poorly understood.We address this question by systematically evaluating neural network architectures featuring combinations of these connections on two synthetic visual tasks, which stress low-level "Gestalt" vs. high-level object cues for perceptual grouping.We show that increasing the difficulty of either task strains learning for networks that rely solely on bottom-up processing.Horizontal connections resolve this limitation on tasks with Gestalt cues by supporting incremental spatial propagation of activities, whereas top-down connections rescue learning on tasks with high-level object cues by modifying coarse predictions about the position of the target object.Our findings dissociate the computational roles of bottom-up, horizontal and top-down connectivity, and demonstrate how a model featuring all of these interactions can more flexibly learn to form perceptual groups.

水平和自上而下的反馈连接是生物和循环视觉系统中互补的知觉分组策略的原因。 利用神经网络作为大脑的计算模型，研究了解决两个视觉挑战的不同策略的效率。


Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images.However, their application in the audio domain has received limited attention,and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech.To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech.Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes.The discriminators analyse the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced.  To measure the performance of GAN-TTS, we employ both subjective human evaluation (MOS - Mean Opinion Score), as well as novel quantitative metrics (FrÃ©chet DeepSpeech Distance and Kernel DeepSpeech Distance), which we find to be well correlated with MOS.We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models, and unlike autoregressive models, it is highly parallelisable thanks to an efficient feed-forward generator.Listen to GAN-TTS reading this abstract at http://tiny.cc/gantts.

我们介绍了GAN-TTS，一个用于文本到语音的生成对抗网络，它达到了平均意见得分（MOS）4.2。

解决了原始波形合成中的GAN挑战，并开始缩小自回归模型和GAN在原始音频中的现有性能差距。