创意支持工具中的反馈可以帮助人群推动他们的意思。但是，目前的反馈方法需要从促进者或同行中进行人力评估。这不可扩展到大人群。我们提出可解释的定向多样性来自动预测观点的质量和多样性分数，并提供AI解释 - 归因，对比归因和反事实建议 - 反馈意见（低），以及如何获得更高的分数。由于用户迭代地提高其想象，这些解释提供了多面反馈。我们进行了形成性和控制的用户研究，以了解解释的使用和有用性，以提高观念多样性和质量。用户感谢解释反馈帮助重点努力，并提供了改进的方向。这导致解释与没有反馈或反馈仅具有预测的反馈和反馈相比提高了多样性。因此，我们的方法为解释和丰富的反馈开辟了可解释的AI的机会，以获得迭代人群思想和创造力支​​持工具。

Real-world datasets exhibit imbalances of varying types and degrees. Several techniques based on re-weighting and margin adjustment of loss are often used to enhance the performance of neural networks, particularly on minority classes. In this work, we analyze the class-imbalanced learning problem by examining the loss landscape of neural networks trained with re-weighting and margin-based techniques. Specifically, we examine the spectral density of Hessian of class-wise loss, through which we observe that the network weights converge to a saddle point in the loss landscapes of minority classes. Following this observation, we also find that optimization methods designed to escape from saddle points can be effectively used to improve generalization on minority classes. We further theoretically and empirically demonstrate that Sharpness-Aware Minimization (SAM), a recent technique that encourages convergence to a flat minima, can be effectively used to escape saddle points for minority classes. Using SAM results in a 6.2\% increase in accuracy on the minority classes over the state-of-the-art Vector Scaling Loss, leading to an overall average increase of 4\% across imbalanced datasets. The code is available at: https://github.com/val-iisc/Saddle-LongTail.

Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for real-time applications on portable devices. This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads. In this project, we developed a real-time UNet based model and deployed that network on Jetson AGX Xavier module.

The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and is known to be NP-hard. This paper proposes GCS-Q, a novel quantum-supported solution for Induced Subgraph Games (ISGs) in coalition structure generation. GCS-Q starts by considering the grand coalition as initial coalition structure and proceeds by iteratively splitting the coalitions into two nonempty subsets to obtain a coalition structure with a higher coalition value. In particular, given an $n$-agent ISG, the GCS-Q solves the optimal split problem $\mathcal{O} (n)$ times using a quantum annealing device, exploring $\mathcal{O}(2^n)$ partitions at each step. We show that GCS-Q outperforms the currently best classical solvers with its runtime in the order of $n^2$ and an expected worst-case approximation ratio of $93\%$ on standard benchmark datasets.

Most existing Spiking Neural Network (SNN) works state that SNNs may utilize temporal information dynamics of spikes. However, an explicit analysis of temporal information dynamics is still missing. In this paper, we ask several important questions for providing a fundamental understanding of SNNs: What are temporal information dynamics inside SNNs? How can we measure the temporal information dynamics? How do the temporal information dynamics affect the overall learning performance? To answer these questions, we estimate the Fisher Information of the weights to measure the distribution of temporal information during training in an empirical manner. Surprisingly, as training goes on, Fisher information starts to concentrate in the early timesteps. After training, we observe that information becomes highly concentrated in earlier few timesteps, a phenomenon we refer to as temporal information concentration. We observe that the temporal information concentration phenomenon is a common learning feature of SNNs by conducting extensive experiments on various configurations such as architecture, dataset, optimization strategy, time constant, and timesteps. Furthermore, to reveal how temporal information concentration affects the performance of SNNs, we design a loss function to change the trend of temporal information. We find that temporal information concentration is crucial to building a robust SNN but has little effect on classification accuracy. Finally, we propose an efficient iterative pruning method based on our observation on temporal information concentration. Code is available at https://github.com/Intelligent-Computing-Lab-Yale/Exploring-Temporal-Information-Dynamics-in-Spiking-Neural-Networks.

我们提出了一个\下划线{d} oully \下划线{o} \下划线{s} afe- \ \ useverline {l} inline {l} inear- \ usew suespline {b}和doslb的问题。安全的线性匪徒问题是使用随机的强盗反馈和动作安全风险的动作来优化未知的线性奖励，同时满足动作的未知圆形安全限制。与先前在汇总资源约束方面的工作相反，我们的公式明确要求控制环形安全风险。与现有的对安全匪徒的乐观态度范式不同，DOSLB练习至高无上，使用对奖励和安全得分的乐观估计来选择动作。然而，令人惊讶的是，我们表明doslb很少采取风险的行动，并获得了$ \ tilde {o}（d \ sqrt {t}）$遗憾，在这里，我们对遗憾的概念既说明效率低下又缺乏行动的安全性。我们首先尤其表明$ \ sqrt {t} $ - 即使有较大的差距也无法改善遗憾的绑定，然后确定我们显示紧密的实例依赖性$ O（\ log（\ log），也无法改善，我们首先表明$ \ sqrt {t} $ - 遗憾的界限也无法改善，我们首先表明$ \ sqrt {t} $ - ^2 t）$边界。我们进一步认为，在这样的域中，播放过度风险的动作的次数也被限制为$ o（\ log^2t）$。

无数据知识蒸馏（DFKD）最近引起了人们的关注，这要归功于其在不使用培训数据的情况下将知识从教师网络转移到学生网络的吸引力。主要思想是使用发电机合成数据以培训学生。随着发电机的更新，合成数据的分布将发生变化。如果发电机和学生接受对手的训练，使学生忘记了先前一步获得的知识，则这种分配转换可能会很大。为了减轻这个问题，我们提出了一种简单而有效的方法，称为动量对抗蒸馏（MAD），该方法维持了发电机的指数移动平均值（EMA）副本，并使用发电机和EMA生成器的合成样品来培训学生。由于EMA发电机可以被视为发电机旧版本的合奏，并且与发电机相比，更新的更改通常会发生较小的变化，因此对其合成样本进行培训可以帮助学生回顾过去的知识，并防止学生适应太快的速度发电机的新更新。我们在六个基准数据集上进行的实验，包括ImageNet和Place365，表明MAD的性能优于竞争方法来处理大型分配转移问题。我们的方法还与现有的DFKD方法相比，甚至在某些情况下达到了最新的方法。

转置卷积在许多深度学习应用中都表现出突出。但是，由于在每个行和列中的每个元素之后添加零之后，特征映射的大小增加，因此转置卷积层在计算范围内都在计算密集型。因此，在扩展的输入特征图上进行的卷积操作导致硬件资源的利用率不佳。不必要的乘法操作的主要原因是在输入特征映射中的预定位置处的零。我们提出了一种算法级优化技术，用于有效的转置卷积实施以解决这些问题。基于内核激活，我们将原始内核隔离为四个子内核。该方案可以减少内存需求和不必要的乘法。我们提出的方法是使用Kaggle网站上的Flower DataSet使用Titan X GPU（Intel Dual Core CPU）的$ 3.09（3.02）\ Times $ $更快的计算。此外，提出的优化方法可以推广到现有设备，而无需其他硬件要求。一个简单的深度学习模型，其中包含一个转齿卷积层来评估优化方法。它显示出使用具有Intel双核CPU的MNIST数据集的$ 2.2 \ times $ $更快的培训。

小波神经网络（WNN）已在许多领域应用于解决回归和分类问题。大数据出现后，随着数据以轻快的速度生成，必须一旦生成，因为数据的性质可能会在短时间间隔发生巨大变化，因此必须立即进行分析。这是必要的，这是必不可少的，那就是大数据全是普遍的，并给数据科学家带来了计算挑战。因此，在本文中，我们构建了一种有效的可扩展，并行的小波神经网络（SPWNN），该神经网络（SPWNN）采用了平行的随机梯度算法（SGD）算法。 SPWNN是在水平并行化框架中的静态和流环境下设计和开发的。 SPWNN是通过使用Morlet和高斯函数作为激活函数来实现的。这项研究是在具有超过400万个样本和医学研究数据等大数据集上进行的，该数据具有超过10,000个功能，其本质上具有很高的尺寸。实验分析表明，在静态环境中，具有Morlet激活函数的SPWNN优于分类数据集上的高斯SPWNN。但是，在回归的情况下，观察到了相反的情况。相反，在流媒体环境中，高斯在分类方面的表现优于莫雷特，而莫雷特在回归数据集上的表现优于高斯。总体而言，拟议的SPWNN体系结构的速度为1.32-1.40。

我们的粮食安全建立在土壤的基础上。如果土壤不健康，农民将无法用纤维，食物和燃料喂养我们。准确预测土壤的类型有助于规划土壤的使用，从而提高生产率。这项研究采用了最先进的视觉变压器，并与SVM，Alexnet，Resnet和CNN等不同模型进行了比较。此外，这项研究还着重于区分不同的视觉变压器体系结构。对于土壤类型的分类，数据集由4种不同类型的土壤样品组成，例如冲积，红色，黑色和粘土。 Visual Transformer模型在测试和测试时达到98.13％的训练和93.62％的范围，在测试和训练精度方面都优于其他模型。视觉变压器的性能超过了其他模型的性能至少2％。因此，新颖的视觉变压器可用于计算机视觉任务，包括土壤分类。