Most previous unsupervised domain adaptation (UDA) methods for question answering(QA) require access to source domain data while fine-tuning the model for the target domain. Source domain data may, however, contain sensitive information and may be restricted. In this study, we investigate a more challenging setting, source-free UDA, in which we have only the pretrained source model and target domain data, without access to source domain data. We propose a novel self-training approach to QA models that integrates a unique mask module for domain adaptation. The mask is auto-adjusted to extract key domain knowledge while trained on the source domain. To maintain previously learned domain knowledge, certain mask weights are frozen during adaptation, while other weights are adjusted to mitigate domain shifts with pseudo-labeled samples generated in the target domain. %As part of the self-training process, we generate pseudo-labeled samples in the target domain based on models trained in the source domain. Our empirical results on four benchmark datasets suggest that our approach significantly enhances the performance of pretrained QA models on the target domain, and even outperforms models that have access to the source data during adaptation.
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This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids. We formulate a resilient reinforcement learning (RL) training setup which (a) generates episodic trajectories injecting adversarial actions at primary control reference signals of the grid forming (GFM) inverters and (b) trains the RL agents (or controllers) to alleviate the impact of the injected adversaries. To circumvent data-sharing issues and concerns for proprietary privacy in multi-party-owned networked grids, we bring in the aspects of federated machine learning and propose a novel Fed-RL algorithm to train the RL agents. To this end, the conventional horizontal Fed-RL approaches using decoupled independent environments fail to capture the coupled dynamics in a networked microgrid, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC) algorithm. We created a customized simulation setup encapsulating microgrid dynamics in the GridLAB-D/HELICS co-simulation platform compatible with the OpenAI Gym interface for training RL agents. Finally, the proposed methodology is validated with numerical examples of modified IEEE 123-bus benchmark test systems consisting of three coupled microgrids.
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本文提出了概率共形预测(PCP),这是一种预测推理算法,该算法通过不连续的预测集估算目标变量。给定输入,PCP基于估计生成模型的随机样品构建预测集。它有效且与显式或隐式有条件生成模型兼容。从理论上讲,我们表明PCP可以保证使用有限样品正确的边际覆盖范围。从经验上讲,我们研究了PCP在各种模拟和真实数据集上。与现有的共形推断方法相比,PCP提供了更清晰的预测集。
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大型语言模型已被证明可以使用少量学习来实现各种自然语言任务的出色表现,这大大减少了将模型调整到特定应用程序所需的特定任务培训示例的数量。为了进一步了解量表对少量学习的影响,我们培训了一个5400亿个参数,密集激活的变压器语言模型,我们称之为“途径”语言模型棕榈。我们使用Pathways在6144 TPU V4芯片上训练了Palm,这是一种新的ML系统,可在多个TPU POD上进行高效的训练。我们通过在数百种语言理解和产生基准的基准方面实现最先进的学习结果来证明扩展的持续好处。在这些任务中,Palm 540B实现了突破性的表现,在一系列多步推理任务上表现出色,超过了最新的最新表现,并且在最近发布的Big Benchmark上表现优于平均人类表现。大量的大型基础任务显示出与模型量表的不连续改进,这意味着当我们扩展到最大模型时,性能急剧增加。 Palm在多语言任务和源代码生成方面也具有很强的功能,我们在各种基准测试中证明了这一点。我们还提供了有关偏见和毒性的全面分析,并研究了训练数据记忆的程度,相对于模型量表。最后,我们讨论与大语言模型有关的道德考虑,并讨论潜在的缓解策略。
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大脑中尖刺神经元之间的沟通的事件驱动和稀疏性质对灵活和节能的AI来说具有很大的承诺。学习算法的最新进展已经证明,与标准经常性神经网络相比,可以有效地培训尖刺神经元的复发网络以实现竞争性能。尽管如此,随着这些学习算法使用错误 - 反复通过时间(BPTT),它们遭受了高的内存要求,慢慢训练,并且与在线学习不兼容。这将这些学习算法的应用限制为相对较小的网络和有限的时间序列长度。已经提出了具有较低计算和内存复杂性的BPTT的在线近似(E-PROP,OSTL),但在实践中也遭受内存限制,并且作为近似,不要倾销标准BPTT训练。在这里,我们展示了最近开发的BPTT替代方法,通过时间(FPTT)可以应用于尖峰神经网络。与BPTT不同,FPTT试图最大限度地减少损失的持续动态正常风险。结果,可以以在线方式计算FPTT,并且相对于序列长度具有固定的复杂性。与新型动态尖刺神经元模型结合时,液态常数神经元,我们表明SNNS培训了FPTT优于在线BPTT近似,并在时间分类任务上接近或超过离线BPTT精度。因此,这种方法使得在长期序列中以记忆友好的在线方式训练SNNS并向新颖和复杂的神经架构进行扩展。
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近年来,图像分类器的BlackBox传输攻击已被广泛研究。相比之下,对对象探测器的转移攻击取得了很小的进展。对象探测器采用图像的整体视图,并检测一个对象(或缺乏)通常取决于场景中的其他对象。这使得这种探测器本质上的上下文感知和对抗的攻击比目标图像分类器更具挑战性。在本文中,我们提出了一种新的方法来为对象检测器生成上下文感知攻击。我们表明,通过使用对象及其相关位置的共同发生和尺寸作为上下文信息,我们可以成功地生成目标的错误分类攻击,该攻击比最先进的Blackbox对象探测器上实现更高的转移成功率。我们在帕斯卡VOC和MS Coco Datasets的各种对象探测器上测试我们的方法,与其他最先进的方法相比,性能提高了高达20美元的百分点。
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我们向连续状态马尔可夫决策过程(MDP)提出了一种扩散近似方法,该方法可用于解决非结构化的越野环境中的自主导航和控制。与呈现完全已知的状态转换模型的大多数决策定理计划框架相比,我们设计了一种方法,该方法消除了这种强烈假设,这些假设通常非常难以在现实中工程师。我们首先采用价值函数的二阶泰勒扩展。然后通过部分微分方程近似贝尔曼的最优性方程,其仅依赖于转换模型的第一和第二矩。通过组合价值函数的内核表示,然后设计一种有效的策略迭代算法,其策略评估步骤可以表示为特征的方程式的线性系统,其特征是由有限组支持状态。我们首先通过大量的仿真以2D美元的$ 2D $避让和2.5d $地形导航问题进行验证。结果表明,拟议的方法在几个基线上导致了卓越的性能。然后,我们开发一个系统,该系统将我们的决策框架整合,与船上感知,并在杂乱的室内和非结构化的户外环境中进行现实世界的实验。物理系统的结果进一步展示了我们在挑战现实世界环境中的方法的适用性。
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鉴于从第一人称角度捕获的视频以及录制视频的环境环境,我们可以认识到该人在做什么并确定3D空间中的动作发生在哪里吗?我们解决了这个具有挑战性的问题,即在以自我为中心视频的已知3D地图上共同识别和本地化操作。为此,我们提出了一种新颖的深层概率模型。我们的模型采用了3D环境的层次体积表示(HVR)的输入和以自我为中心的视频,将3D Action位置视为潜在变量,并根据其潜在位置的视频和上下文提示识别动作。为了评估我们的模型,我们对EGO4D数据集的子集进行了广泛的实验,其中捕获了人类自然主义的作用和照片现实的3D环境重建。我们的方法证明了在可见和看不见的环境之间进行动作识别和3D动作定位的强劲结果。我们认为,我们的工作指向以自我为中心的视觉和3D场景理解的相交的令人兴奋的研究方向。
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The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.
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