自我监督学习（SSL）被视为一种非常有前途的方法，对于下游任务的几个语音，高性能。由于SSL模型的参数通常是如此之大，以至于训练和推理需要大量的内存和计算成本，因此希望通过应用诸如知识蒸馏（KD）等压缩方法来生成紧凑的SSL模型，而无需显着性能降解。尽管KD方法能够缩小SSL模型结构的深度和/或宽度，但几乎没有研究如何改变深度和宽度对小脚印模型的内部表示。本文提供了一项解决问题的经验研究。我们在改变结构和KD方法的同时研究了Superb的性能，以保持参数恒定的数量；这使我们能够分析通过改变模型体系结构引入的表示的贡献。实验表明，一定深度对于准确地求解面向内容的任务（例如自动语音识别）至关重要，而在几个面向讲话者的任务上（例如，说话者的身份），必须进行一定宽度对于实现高性能。基于这些观察结果，我们确定了与以前的研究相比，具有更好性能的更高压模型。

本文提出了一种用于端到端现场文本识别的新颖培训方法。端到端的场景文本识别提供高识别精度，尤其是在使用基于变压器的编码器 - 解码器模型时。要培训高度准确的端到端模型，我们需要为目标语言准备一个大型图像到文本配对数据集。但是，很难收集这些数据，特别是对于资源差的语言。为了克服这种困难，我们所提出的方法利用富裕的大型数据集，以资源丰富的语言，如英语，培训资源差的编码器解码器模型。我们的主要思想是建立一个模型，其中编码器反映了多种语言的知识，而解码器专门从事资源差的语言。为此，所提出的方法通过使用组合资源贫乏语言数据集和资源丰富的语言数据集的多语言数据集来预先培训编码器，以学习用于场景文本识别的语言不变知识。所提出的方法还通过使用资源贫乏语言的数据集预先列举解码器，使解码器更适合资源较差的语言。使用小型公共数据集进行日本现场文本识别的实验证明了该方法的有效性。

本文提出了一种用于对话序列标记的新型知识蒸馏方法。对话序列标签是监督的学习任务，估计目标对话文档中每个话语的标签，并且对于许多诸如对话法估计的许多应用是有用的。准确的标签通常通过分层结构化的大型模型来实现，这些大型模型组成的话语级和对话级网络，分别捕获话语内和话语之间的上下文。但是，由于其型号大小，因此无法在资源受限设备上部署此类模型。为了克服这种困难，我们专注于通过蒸馏了大型和高性能教师模型的知识来列举一个小型模型的知识蒸馏。我们的主要思想是蒸馏知识，同时保持教师模型捕获的复杂环境。为此，所提出的方法，等级知识蒸馏，通过蒸馏来列举小型模型，而不是通过培训模型在教师模型中培训的话语水平和对话级环境的知识模拟教师模型在每个级别的输出。对话法案估算和呼叫场景分割的实验证明了该方法的有效性。

When simulating soft robots, both their morphology and their controllers play important roles in task performance. This paper introduces a new method to co-evolve these two components in the same process. We do that by using the hyperNEAT algorithm to generate two separate neural networks in one pass, one responsible for the design of the robot body structure and the other for the control of the robot. The key difference between our method and most existing approaches is that it does not treat the development of the morphology and the controller as separate processes. Similar to nature, our method derives both the "brain" and the "body" of an agent from a single genome and develops them together. While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects "brain" and the "body" at the same time. Additionally, we present a new speciation function that takes into consideration both the genotypic distance, as is the standard for NEAT, and the similarity between robot bodies. By using this function, agents with very different bodies are more likely to be in different species, this allows robots with different morphologies to have more specialized controllers since they won't crossover with other robots that are too different from them. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster when compared to having separated genomes for body and control. The agents in our population also show morphologies with a high degree of regularity and controllers capable of coordinating the voxels to produce the necessary movements.

For arbitrary two probability measures on real d-space with given means and variances (covariance matrices), we provide lower bounds for their total variation distance.

Taking into account background knowledge as the context has always been an important part of solving tasks that involve natural language. One representative example of such tasks is text-based games, where players need to make decisions based on both description text previously shown in the game, and their own background knowledge about the language and common sense. In this work, we investigate not simply giving common sense, as can be seen in prior research, but also its effective usage. We assume that a part of the environment states different from common sense should constitute one of the grounds for action selection. We propose a novel agent, DiffG-RL, which constructs a Difference Graph that organizes the environment states and common sense by means of interactive objects with a dedicated graph encoder. DiffG-RL also contains a framework for extracting the appropriate amount and representation of common sense from the source to support the construction of the graph. We validate DiffG-RL in experiments with text-based games that require common sense and show that it outperforms baselines by 17% of scores. The code is available at https://github.com/ibm/diffg-rl

Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCup@Home Japan open competition open platform league every year since 2010. Moreover, we participated in the RoboCup 2017 Nagoya as open platform league and domestic standard platform league teams. Currently, the Hibikino-Musashi@Home team has 20 members from seven different laboratories based in the Kyushu Institute of Technology. In this paper, we introduce the activities of our team and the technologies.

本文档描述了Spotify出于学术研究目的发布的葡萄牙语播客数据集。我们概述了如何采样数据，有关集合的一些基本统计数据，以及有关巴西和葡萄牙方言的分发信息的简要信息。

深度神经网络（DNN）众所周知，很容易受到对抗例子的影响（AES）。此外，AE具有对抗性可传递性，这意味着为源模型生成的AE可以以非平凡的概率欺骗另一个黑框模型（目标模型）。在本文中，我们首次研究了包括Convmixer在内的模型之间的对抗性转移性的属性。为了客观地验证可转让性的属性，使用称为AutoAttack的基准攻击方法评估模型的鲁棒性。在图像分类实验中，Convmixer被确认对对抗性转移性较弱。

场景中光的极化信息对于各种图像处理和计算机视觉任务很有价值。平面偏光仪是一种有前途的方法，可以一次性地捕获不同方向的极化图像，而它需要颜色极化的表现。在本文中，我们提出了一个两步的颜色偏振化学网络〜（TCPDNET），该网络由两个颜色的表演和极化演示组成。我们还引入了YCBCR颜色空间中的重建损失，以提高TCPDNET的性能。实验比较表明，TCPDNET在极化图像的图像质量和Stokes参数的准确性方面优于现有方法。