Large pretrained Transformer-based language models like BERT and GPT have changed the landscape of Natural Language Processing (NLP). However, fine tuning such models still requires a large number of training examples for each target task, thus annotating multiple datasets and training these models on various downstream tasks becomes time consuming and expensive. In this work, we propose a simple extension of the Prototypical Networks for few-shot text classification. Our main idea is to replace the class prototypes by Gaussians and introduce a regularization term that encourages the examples to be clustered near the appropriate class centroids. Experimental results show that our method outperforms various strong baselines on 13 public and 4 internal datasets. Furthermore, we use the class distributions as a tool for detecting potential out-of-distribution (OOD) data points during deployment.
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变形金刚是最先进的深度学习模型，由堆叠的注意力和点亮的完全连接的层组成，用于处理顺序数据。变压器不仅普遍存在于自然语言处理（NLP），而且最近，他们启发了一股新的计算机视觉（CV）应用研究。在这项工作中，应用视觉变压器（VIT）以预测二维介绍模型模拟的状态变量。我们的实验表明，当使用来自对应于各种边界条件和温度的鉴定模型的少量微生物图像时，vit utem最先进的卷积神经网络（CNN）。这项工作开辟了应用vit的其他模拟的可能性，并提高了有趣的研究方向，了解Lepence Maps如何了解不同现象的基础物理学。
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在本文中，据我们所知，我们提供了将各种掩盖机制纳入变形金刚以可扩展方式融入变形金刚结构的第一种综合方法。我们表明，有关线性因果关注的最新结果（Choromanski等，2021）和对数线性RPE注意力（Luo等，2021）是这种一般机制的特殊情况。但是，通过将问题作为对未掩盖注意力的拓扑调制（基于图的）调制，我们以前获得了几个未知结果，包括有效的D维RPE掩盖和图形内掩蔽。我们利用许多数学技术，从光谱分析到动态编程和随机步行到新算法，以求解图形上的马尔可夫过程。我们提供相应的经验评估。
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在本报告中，我们提出了一个深入的学习框架，称为电子相关潜在神经网络（ECPNN），可以学习简洁和紧凑的潜在功能。这些功能可以有效地描述许多电子原子中电子之间的复杂瞬时空间相关性。ECPNN以无监督的方式培训，从完全配置相互作用（FCI）一电子密度函数的有限信息，在预定义的准确性范围内。使用ECPNN生成的有效相关势函数，我们可以在与FCI能量相比时预测每个研究的原子系统的总能量具有显着的精度。
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Inspired by strategies like Active Learning, it is intuitive that intelligently selecting the training classes from a dataset for Zero-Shot Learning (ZSL) can improve the performance of existing ZSL methods. In this work, we propose a framework called Diverse and Rare Class Identifier (DiRaC-I) which, given an attribute-based dataset, can intelligently yield the most suitable "seen classes" for training ZSL models. DiRaC-I has two main goals - constructing a diversified set of seed classes, followed by a visual-semantic mining algorithm initialized by these seed classes that acquires the classes capturing both diversity and rarity in the object domain adequately. These classes can then be used as "seen classes" to train ZSL models for image classification. We adopt a real-world scenario where novel object classes are available to neither DiRaC-I nor the ZSL models during training and conducted extensive experiments on two benchmark data sets for zero-shot image classification - CUB and SUN. Our results demonstrate DiRaC-I helps ZSL models to achieve significant classification accuracy improvements.
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Modern telecom systems are monitored with performance and system logs from multiple application layers and components. Detecting anomalous events from these logs is key to identify security breaches, resource over-utilization, critical/fatal errors, etc. Current supervised log anomaly detection frameworks tend to perform poorly on new types or signatures of anomalies with few or unseen samples in the training data. In this work, we propose a meta-learning-based log anomaly detection framework (LogAnMeta) for detecting anomalies from sequence of log events with few samples. LoganMeta train a hybrid few-shot classifier in an episodic manner. The experimental results demonstrate the efficacy of our proposed method
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Zero-shot detection (ZSD) is a challenging task where we aim to recognize and localize objects simultaneously, even when our model has not been trained with visual samples of a few target ("unseen") classes. Recently, methods employing generative models like GANs have shown some of the best results, where unseen-class samples are generated based on their semantics by a GAN trained on seen-class data, enabling vanilla object detectors to recognize unseen objects. However, the problem of semantic confusion still remains, where the model is sometimes unable to distinguish between semantically-similar classes. In this work, we propose to train a generative model incorporating a triplet loss that acknowledges the degree of dissimilarity between classes and reflects them in the generated samples. Moreover, a cyclic-consistency loss is also enforced to ensure that generated visual samples of a class highly correspond to their own semantics. Extensive experiments on two benchmark ZSD datasets - MSCOCO and PASCAL-VOC - demonstrate significant gains over the current ZSD methods, reducing semantic confusion and improving detection for the unseen classes.
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深度学习（DL）系统的安全性是一个极为重要的研究领域，因为它们正在部署在多个应用程序中，因为它们不断改善，以解决具有挑战性的任务。尽管有压倒性的承诺，但深度学习系统容易受到制作的对抗性例子的影响，这可能是人眼无法察觉的，但可能会导致模型错误分类。对基于整体技术的对抗性扰动的保护已被证明很容易受到更强大的对手的影响，或者证明缺乏端到端评估。在本文中，我们试图开发一种新的基于整体的解决方案，该解决方案构建具有不同决策边界的防御者模型相对于原始模型。通过（1）通过一种称为拆分和剃须的方法转换输入的分类器的合奏，以及（2）通过一种称为对比度功能的方法限制重要特征，显示出相对于相对于不同的梯度对抗性攻击，这减少了将对抗性示例从原始示例转移到针对同一类的防御者模型的机会。我们使用标准图像分类数据集（即MNIST，CIFAR-10和CIFAR-100）进行了广泛的实验，以实现最新的对抗攻击，以证明基于合奏的防御的鲁棒性。我们还在存在更强大的对手的情况下评估稳健性，该对手同时靶向合奏中的所有模型。已经提供了整体假阳性和误报的结果，以估计提出的方法的总体性能。
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具有基于框架的相机的光流计算具有很高的精度，但速度受算法的模型大小或相机帧速率的限制。这使得它不适用于高速应用程序。事件摄像机提供连续的异步事件流，以克服帧速率限制。但是，用于处理数据借用框架之类的算法，例如设置限制速度，或者遭受较低的精度。我们融合了框架和基于事件的管道的互补准确性和速度优势，以提供高速光流，同时保持较低的错误率。我们的生物模仿网络通过MVSEC数据集进行了验证，该数据集以4倍速度上升显示了19％的误差降解。然后，我们通过高速无人机飞行方案演示了系统，该方案甚至在光学摄像头看到无人机使其适用于跟踪和分割等应用程序之前，高速事件摄像头也能计算流程。这项工作表明，可以通过融合来自其他方式的数据来克服基于框架的处理中的基本权衡。
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我们提出了MDEAW，这是一个多模式数据库，该数据库由电动活动（EDA）和光摄影学（PPG）信号组成，在考试期间记录了巴塞罗那萨巴德尔（Eurecat Academemy）的老师教师教授的课程，以引起对学生对学生对情感反应的情感反应。课堂场景。以6种基本的情感状态来记录了10名学生的信号以及学生对每个刺激后对情感状态的自我评估。所有信号均使用便携式，可穿戴，无线，低成本和现成的设备捕获，该设备有可能在日常应用中使用情感计算方法。使用基于EDA和PPG的功能及其融合的学生识别的基线是通过remecs，fed-emecs和fed-emecs-u建立的。这些结果表明，使用低成本设备进行情感状态识别应用的前景。提出的数据库将公开可用，以使研究人员能够对这些捕获设备对情绪状态识别应用的适用性进行更透彻的评估。
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