基于深度学习的渠道代码设计最近引起了人们的兴趣，可以替代传统的编码算法，尤其是对于现有代码不提供有效解决方案的渠道。通过反馈渠道进行的沟通就是一个这样的问题，最近通过采用各种深度学习体系结构来获得有希望的结果。在本文中，我们为反馈渠道介绍了一种新颖的学习辅助代码设计，称为广义块注意反馈（GBAF）代码，i）使用模块化体系结构，可以使用不同的神经网络体系结构实现；ii）与现有设计相比，错误的可能性提高了误顺序；iii）可以以所需的代码速率传输。

Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based communication paradigm that exploits feedback from the receiver. We present AttentionCode, a new class of feedback codes leveraging deep learning (DL) technologies. The underpinnings of AttentionCode are three architectural innovations: AttentionNet, input restructuring, and adaptation to fading channels, accompanied by several training methods, including large-batch training, distributed learning, look-ahead optimizer, training-test signal-to-noise ratio (SNR) mismatch, and curriculum learning. The training methods can potentially be generalized to other wireless communication applications with machine learning. Numerical experiments verify that AttentionCode establishes a new state of the art among all DL-based feedback codes in both additive white Gaussian noise (AWGN) channels and fading channels. In AWGN channels with noiseless feedback, for example, AttentionCode achieves a block error rate (BLER) of $10^{-7}$ when the forward channel SNR is 0 dB for a block size of 50 bits, demonstrating the potential of AttentionCode to provide ultra-reliable short-packet communications.

Many practical applications, such as recommender systems and learning to rank, involve solving multiple similar tasks. One example is learning of recommendation policies for users with similar movie preferences, where the users may still rank the individual movies slightly differently. Such tasks can be organized in a hierarchy, where similar tasks are related through a shared structure. In this work, we formulate this problem as a contextual off-policy optimization in a hierarchical graphical model from logged bandit feedback. To solve the problem, we propose a hierarchical off-policy optimization algorithm (HierOPO), which estimates the parameters of the hierarchical model and then acts pessimistically with respect to them. We instantiate HierOPO in linear Gaussian models, for which we also provide an efficient implementation and analysis. We prove per-task bounds on the suboptimality of the learned policies, which show a clear improvement over not using the hierarchical model. We also evaluate the policies empirically. Our theoretical and empirical results show a clear advantage of using the hierarchy over solving each task independently.

We present a method for controlling a swarm using its spectral decomposition -- that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain -- guaranteeing scale invariance with respect to the number of agents both for computation and for the operator tasked with controlling the swarm. We use ergodic control, decentralized across the network, for implementation. In the DARPA OFFSET program field setting, we test this interface design for the operator using the STOMP interface -- the same interface used by Raytheon BBN throughout the duration of the OFFSET program. In these tests, we demonstrate that our approach is scale-invariant -- the user specification does not depend on the number of agents; it is persistent -- the specification remains active until the user specifies a new command; and it is real-time -- the user can interact with and interrupt the swarm at any time. Moreover, we show that the spectral/ergodic specification of swarm behavior degrades gracefully as the number of agents goes down, enabling the operator to maintain the same approach as agents become disabled or are added to the network. We demonstrate the scale-invariance and dynamic response of our system in a field relevant simulator on a variety of tactical scenarios with up to 50 agents. We also demonstrate the dynamic response of our system in the field with a smaller team of agents. Lastly, we make the code for our system available.

Novel view synthesis and 3D modeling using implicit neural field representation are shown to be very effective for calibrated multi-view cameras. Such representations are known to benefit from additional geometric and semantic supervision. Most existing methods that exploit additional supervision require dense pixel-wise labels or localized scene priors. These methods cannot benefit from high-level vague scene priors provided in terms of scenes' descriptions. In this work, we aim to leverage the geometric prior of Manhattan scenes to improve the implicit neural radiance field representations. More precisely, we assume that only the knowledge of the scene (under investigation) being Manhattan is known - with no additional information whatsoever - with an unknown Manhattan coordinate frame. Such high-level prior is then used to self-supervise the surface normals derived explicitly in the implicit neural fields. Our modeling allows us to group the derived normals, followed by exploiting their orthogonality constraints for self-supervision. Our exhaustive experiments on datasets of diverse indoor scenes demonstrate the significant benefit of the proposed method over the established baselines.

Classification of cancer cellularity within tissue samples is currently a manual process performed by pathologists. This process of correctly determining cancer cellularity can be time intensive. Deep Learning (DL) techniques in particular have become increasingly more popular for this purpose, due to the accuracy and performance they exhibit, which can be comparable to the pathologists. This work investigates the capabilities of two DL approaches to assess cancer cellularity in whole slide images (WSI) in the SPIE-AAPM-NCI BreastPathQ challenge dataset. The effects of training on augmented data via rotations, and combinations of multiple architectures into a single network were analyzed using a modified Kendall Tau-b prediction probability metric known as the average prediction probability PK. A deep, transfer learned, Convolutional Neural Network (CNN) InceptionV3 was used as a baseline, achieving an average PK value of 0.884, showing improvement from the average PK value of 0.83 achieved by pathologists. The network was then trained on additional training datasets which were rotated between 1 and 360 degrees, which saw a peak increase of PK up to 4.2%. An additional architecture consisting of the InceptionV3 network and VGG16, a shallow, transfer learned CNN, was combined in a parallel architecture. This parallel architecture achieved a baseline average PK value of 0.907, a statistically significantly improvement over either of the architectures' performances separately (p<0.0001 by unpaired t-test).

有限的公开数据可以支持恶意软件分析技术的研究。特别是，几乎没有由杜鹃/斗篷等丰富的沙盒生成的公开可用数据集。使用动态沙箱的好处是对目标机中文件执行的逼真模拟并获得该执行日志。机器可以被恶意软件感染，因此很有可能在执行日志中捕获恶意行为，从而使研究人员可以详细研究这种行为。尽管随后对日志信息的分析在工业网络安全后端被广泛介绍，但据我们所知，仅在学术界投入了有限的努力，以使用最先进的技术提高此类日志分析功能。我们使此示例数据集可用来支持设计新的机器学习方法以进行恶意软件检测，尤其是用于自动检测通用恶意行为。该数据集是在Avast软件和捷克技术大学-AI中心（AIC）之间合作的。

从原始数据输入中学习，因此限制了功能工程的需求，是机器学习方法在各个域中的许多成功应用的组成部分。尽管许多问题自然地转化为直接在标准分类器中使用的矢量表示形式，但许多数据源具有结构化数据互换格式的自然形式（例如，以JSON/XML格式使用的安全日志）。现有方法，例如在层次多实例学习（HMIL）中，允许以原始形式从此类数据中学习。但是，对原始结构化数据培训的分类器的解释仍然在很大程度上尚未探索。通过将这些模型视为子集选择问题，我们证明了如何使用计算有效算法来生成具有优惠属性的可解释解释。我们与图形神经网络采用的解释技术进行比较，该技术显示了速度加速和更高质量的解释的顺序。

我们介绍了一个多臂强盗模型，其中奖励是多个随机变量的总和，每个动作只会改变其中的分布。每次动作之后，代理都会观察所有变量的实现。该模型是由营销活动和推荐系统激励的，在该系统中，变量代表单个客户的结果，例如点击。我们提出了UCB风格的算法，以估计基线上的动作的提升。我们研究了问题的多种变体，包括何时未知基线和受影响的变量，并证明所有这些变量均具有sublrinear后悔界限。我们还提供了较低的界限，以证明我们的建模假设的必要性是合理的。关于合成和现实世界数据集的实验显示了估计不使用这种结构的策略的振奋方法的好处。

非政策学习是使用另一个策略收集的数据优化政策而无需部署政策的框架。在推荐系统中，由于记录数据的不平衡问题尤其具有挑战性：建议某些项目比其他项目更频繁地记录。推荐项目列表时，这将进一步延续，因为动作空间是组合的。为了应对这一挑战，我们研究了对学习排名的悲观非政策优化。关键想法是在点击模型的参数上计算较低的置信度范围，然后以最高的悲观估计值返回列表。这种方法在计算上是有效的，我们对其进行了分析。我们研究其贝叶斯和频繁的变体，并通过合并经验贝叶斯来克服未知先验的局限性。为了展示我们方法的经验有效性，我们将其与使用反向倾向得分或忽略不确定性的非政策优化器进行了比较。我们的方法的表现优于所有基线，也是强大的，并且也是一般的。