This work proposes Multi-task Meta Learning (MTML), integrating two learning paradigms Multi-Task Learning (MTL) and meta learning, to bring together the best of both worlds. In particular, it focuses simultaneous learning of multiple tasks, an element of MTL and promptly adapting to new tasks with fewer data, a quality of meta learning. It is important to highlight that we focus on heterogeneous tasks, which are of distinct kind, in contrast to typically considered homogeneous tasks (e.g., if all tasks are classification or if all tasks are regression tasks). The fundamental idea is to train a multi-task model, such that when an unseen task is introduced, it can learn in fewer steps whilst offering a performance at least as good as conventional single task learning on the new task or inclusion within the MTL. By conducting various experiments, we demonstrate this paradigm on two datasets and four tasks: NYU-v2 and the taskonomy dataset for which we perform semantic segmentation, depth estimation, surface normal estimation, and edge detection. MTML achieves state-of-the-art results for most of the tasks. Although semantic segmentation suffers quantitatively, our MTML method learns to identify segmentation classes absent in the pseudo labelled ground truth of the taskonomy dataset.

这项工作提出了一种新型的自我监督的预训练方法，以学习有效的表示，而没有在组织病理学医学图像上使用放大倍率的因素进行标签。其他最先进的工作主要集中在完全监督的学习方法上，这些学习方法严重依赖人类注释。但是，标记和未标记数据的稀缺性是组织病理学的长期挑战。当前，没有标签的表示学习仍未探索组织病理学领域。提出的方法是放大事先的对比相似性（MPC），可以通过利用放大倍率，电感转移和减少人类先验的宽度乳腺癌数据集中的无标签来进行自我监督的学习。当仅20％的标签用于微调和表现以前的工作中，在完全监督的学习环境中，该方法与恶性分类的最新学习相匹配。它提出了一个假设，并提供了经验证据来支持，从而减少人类优先导致自学​​中有效表示学习。这项工作的实施可在github-https：//github.com/prakashchhipa/magnification-prior-self-supervised-method上在线获得。

整合不同域的知识是人类学习的重要特征。学习范式如转移学习，元学习和多任务学习，通过利用新任务的先验知识，鼓励更快的学习和新任务的良好普遍来反映人类学习过程。本文提供了这些学习范例的详细视图以及比较分析。学习算法的弱点是另一个的力量，从而合并它们是文献中的一种普遍的特征。这项工作提供了对文章的文献综述，这些文章融合了两种算法来完成多个任务。这里还介绍了全球通用学习网络，在此介绍了元学习，转移学习和多任务学习的集合，以及一些开放的研究问题和未来研究的方向。

Metric Elicitation (ME) is a framework for eliciting classification metrics that better align with implicit user preferences based on the task and context. The existing ME strategy so far is based on the assumption that users can most easily provide preference feedback over classifier statistics such as confusion matrices. This work examines ME, by providing a first ever implementation of the ME strategy. Specifically, we create a web-based ME interface and conduct a user study that elicits users' preferred metrics in a binary classification setting. We discuss the study findings and present guidelines for future research in this direction.

The first large-scale deployment of private federated learning uses differentially private counting in the continual release model as a subroutine (Google AI blog titled "Federated Learning with Formal Differential Privacy Guarantees"). In this case, a concrete bound on the error is very relevant to reduce the privacy parameter. The standard mechanism for continual counting is the binary mechanism. We present a novel mechanism and show that its mean squared error is both asymptotically optimal and a factor 10 smaller than the error of the binary mechanism. We also show that the constants in our analysis are almost tight by giving non-asymptotic lower and upper bounds that differ only in the constants of lower-order terms. Our algorithm is a matrix mechanism for the counting matrix and takes constant time per release. We also use our explicit factorization of the counting matrix to give an upper bound on the excess risk of the private learning algorithm of Denisov et al. (NeurIPS 2022). Our lower bound for any continual counting mechanism is the first tight lower bound on continual counting under approximate differential privacy. It is achieved using a new lower bound on a certain factorization norm, denoted by $\gamma_F(\cdot)$, in terms of the singular values of the matrix. In particular, we show that for any complex matrix, $A \in \mathbb{C}^{m \times n}$, \[ \gamma_F(A) \geq \frac{1}{\sqrt{m}}\|A\|_1, \] where $\|\cdot \|$ denotes the Schatten-1 norm. We believe this technique will be useful in proving lower bounds for a larger class of linear queries. To illustrate the power of this technique, we show the first lower bound on the mean squared error for answering parity queries.

The presence of bias in deep models leads to unfair outcomes for certain demographic subgroups. Research in bias focuses primarily on facial recognition and attribute prediction with scarce emphasis on face detection. Existing studies consider face detection as binary classification into 'face' and 'non-face' classes. In this work, we investigate possible bias in the domain of face detection through facial region localization which is currently unexplored. Since facial region localization is an essential task for all face recognition pipelines, it is imperative to analyze the presence of such bias in popular deep models. Most existing face detection datasets lack suitable annotation for such analysis. Therefore, we web-curate the Fair Face Localization with Attributes (F2LA) dataset and manually annotate more than 10 attributes per face, including facial localization information. Utilizing the extensive annotations from F2LA, an experimental setup is designed to study the performance of four pre-trained face detectors. We observe (i) a high disparity in detection accuracies across gender and skin-tone, and (ii) interplay of confounding factors beyond demography. The F2LA data and associated annotations can be accessed at http://iab-rubric.org/index.php/F2LA.

小型模块化反应堆的概念改变了解决未来能源危机的前景。考虑到其较低的投资要求，模块化，设计简单性和增强的安全功能，这种新的反应堆技术非常有希望。人工智能驱动的多尺度建模（中子，热液压，燃料性能等）在小型模块化反应堆的研究中纳入了数字双胞胎和相关的不确定性。在这项工作中，进行了一项关于耐亡燃料的多尺度建模的全面研究。探索了这些燃料在轻水的小型模块化反应堆中的应用。本章还重点介绍了机器学习和人工智能在设计优化，控制和监视小型模块反应器中的应用。最后，简要评估了有关人工智能在高燃烧复合事故耐受燃料的发展中的研究差距。还讨论了实现这些差距的必要行动。

DeepFake是指量身定制和合成生成的视频，这些视频现在普遍存在并大规模传播，威胁到在线可用信息的可信度。尽管现有的数据集包含不同类型的深击，但它们的生成技术各不相同，但它们并不考虑以“系统发育”方式进展。现有的深层面孔可能与另一个脸交换。可以多次执行面部交换过程，并且可以演变出最终的深层效果，以使DeepFake检测算法混淆。此外，许多数据库不提供应用的生成模型作为目标标签。模型归因通过提供有关所使用的生成模型的信息，有助于增强检测结果的解释性。为了使研究界能够解决这些问题，本文提出了Deephy，这是一种新型的DeepFake系统发育数据集，由使用三种不同的一代技术生成的5040个DeepFake视频组成。有840个曾经交换深击的视频，2520个换两次交换深击的视频和1680个换装深击的视频。使用超过30 GB的大小，使用1,352 GB累积内存的18 GPU在1100多个小时内准备了数据库。我们还使用六种DeepFake检测算法在Deephy数据集上展示了基准。结果突出了需要发展深击模型归因的研究，并将过程推广到各种深层生成技术上。该数据库可在以下网址获得：http：//iab-rubric.org/deephy-database

除了独奏游戏外，棋盘游戏至少需要其他玩家才能玩。因此，当对手失踪时，我们创建了人工智能（AI）代理商来对抗我们。这些AI代理是通过多种方式创建的，但是这些代理的一个挑战是，与我们相比，代理可以具有较高的能力。在这项工作中，我们描述了如何创建玩棋盘游戏的较弱的AI代理。我们使用Tic-Tac-toe，九名成员的莫里斯和曼卡拉，我们的技术使用了增强学习模型，代理商使用Q学习算法来学习这些游戏。我们展示了这些代理商如何学会完美地玩棋盘游戏，然后我们描述了制作这些代理商较弱版本的方法。最后，我们提供了比较AI代理的方法。

在启用语音的应用程序中，一个预定的热词在同时用来激活设备以便进行查询。 toavoid重复一个热词，我们提出了一个端到端的流（E2E）打算查询检测器，该查询检测器识别向设备指向的发音，并滤除针对设备的其他发出内容。提出的方法将预期的查询检测器置于E2E模型中，该模型将语音识别的不同组件折叠成一个神经网络。E2E对台面解码和预期的查询检测进行建模，也使我们可以基于早期的部分偏置检测结果， ，这对于减少潜伏期和使系统响应很重要。我们证明，与独立的预期检测器相比，检测准确性和600个MSLATENCE的相对相对改善的相对提高一级误差率（EER）的相对提高了22％。在我们的实验中，提出的模型检测用户正在用用户开始讲话后，用8.7％的Eerwithin与设备进行对话。