表征过度参数化神经网络的显着概括性能仍然是一个开放的问题。在本文中，我们促进了将重点转移到初始化而不是神经结构或（随机）梯度下降的转变，以解释这种隐式的正则化。通过傅立叶镜头，我们得出了神经网络光谱偏置的一般结果，并表明神经网络的概括与它们的初始化密切相关。此外，我们在经验上使用实用的深层网络巩固了开发的理论见解。最后，我们反对有争议的平米尼猜想，并表明傅立叶分析为理解神经网络的概括提供了更可靠的框架。

The necessity of data driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a Healthcare Provider Facility or a Hospital (from here on termed as Facility) Market Share is of key importance. This pilot study aims at developing a data driven Machine Learning - Regression framework which aids strategists in formulating key decisions to improve the Facilitys Market Share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study; and the data spanning across 60 key Facilities in Washington State and about 3 years of historical data is considered. In the current analysis Market Share is termed as the ratio of facility encounters to the total encounters among the group of potential competitor facilities. The current study proposes a novel two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. The proposed method to identify pool of competitors in current analysis, develops Directed Acyclic Graphs (DAGs), feature level word vectors and evaluates the key connected components at facility level. This technique is robust since its data driven which minimizes the bias from empirical techniques. Post identifying the set of competitors among facilities, developed Regression model to predict the Market share. For relative quantification of features at a facility level, incorporated SHAP a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share.

The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets. However, in realistic scenarios, finding an optimal source domain with identical label space is a challenging task. Partial domain adaptation alleviates this problem of procuring a labeled dataset with identical label space assumptions and addresses a more practical scenario where the source label set subsumes the target label set. This, however, presents a few additional obstacles during adaptation. Samples with categories private to the source domain thwart relevant knowledge transfer and degrade model performance. In this work, we try to address these issues by coupling variational information and adversarial learning with a pseudo-labeling technique to enforce class distribution alignment and minimize the transfer of superfluous information from the source samples. The experimental findings in numerous cross-domain classification tasks demonstrate that the proposed technique delivers superior and comparable accuracy to existing methods.

We introduce a general theoretical framework, designed for the study of gradient optimisation of deep neural networks, that encompasses ubiquitous architectural choices including batch normalisation, weight normalisation and skip connections. We use our framework to conduct a global analysis of the curvature and regularity properties of neural network loss landscapes induced by normalisation layers and skip connections respectively. We then demonstrate the utility of this framework in two respects. First, we give the only proof of which we are presently aware that a class of deep neural networks can be trained using gradient descent to global optima even when such optima only exist at infinity, as is the case for the cross-entropy cost. Second, we verify a prediction made by the theory, that skip connections accelerate training, with ResNets on MNIST, CIFAR10, CIFAR100 and ImageNet.

人工智能在医学成像，尤其是组织病理学成像方面具有巨大的希望。但是，人工智能算法无法完全解释决策过程中的思维过程。这种情况带来了解释性的问题，即黑匣子问题，人工智能应用程序的议程：一种算法只是在没有说明给定图像的原因的情况下做出响应。为了克服问题并提高解释性，可解释的人工智能（XAI）脱颖而出，并激发了许多研究人员的利益。在此背景下，本研究使用深度学习算法检查了一个新的原始数据集，并使用XAI应用程序之一（GRAD-CAM）可视化输出。之后，对这些图像的病理学家进行了详细的问卷调查。决策过程和解释都已验证，并测试了输出的准确性。这项研究的结果极大地帮助病理学家诊断旁结核病。

与标准闭合域的适应任务相反，部分域适应设置通过放松相同的标签集假设来迎合现实情况。但是，源标签集集成了目标标签集的事实，因此引入了一些额外的障碍，因为私人源类别样本的培训阻止了相关的知识转移并误导了分类过程。为了减轻这些问题，我们设计了一种机制，用于策略选择高度自信的目标样本，这对于估算班级的体重所必需的必不可少的机制。此外，我们通过将实现紧凑型和不同类别分布的过程与对抗性目标结合过程来捕获类歧视和域的不变特征。对众多跨域分类任务的实验发现证明了所提出的技术具有比现有方法具有卓越和可比精度的潜力。

图像到图像翻译（I2I）是一个充满挑战的计算机视觉问题，用于多个任务的众多域。最近，眼科成为I2i的应用迅速增加的主要领域之一。一种这样的应用是合成视网膜光学相干断层（OCT）扫描的产生。现有的I2I方法需要培训多种模型，将图像从正常扫描转换为特定病理学：限制由于它们的复杂性而对这些模型的使用。要解决此问题，我们提出了一个无监督的多域I2I网络，具有预先培训的样式编码器，可将一个域中的视网膜OCT图像转换为多个域。我们假设图像分裂到域不变内容和域特定的样式代码，并预先培训这些样式代码。所执行的实验表明，所提出的模型优于Munit和Cyclangan合成不同的病理扫描等最先进的模型。

截至较晚的许多域名正在使用人工智能，法律制度也不例外。然而，正如现在所掌握的那样，来自美国最高法院（Scotus）的法律文件的良好注释数据集的数量非常有限。尽管最高法院裁决是公共领域的知识，但由于需要手动收集和处理每次划痕的数据，因此尝试与他们有意义的工作成为更大的任务。因此，我们的目标是创建Scotus法庭案件的高质量数据集，以便可以随时用于自然语言处理（NLP）研究和其他数据驱动应用程序。此外，NLP的最新进展为我们提供了构建可用于揭示影响法院决策的模式的预测模型的工具。通过使用先进的NLP算法来分析以前的法庭案件，训练有素的模型能够预测和分类法院的判断，鉴于原告和被告的文本格式的事实;换句话说，该模型正在通过产生最终判决来模拟人类陪审团。

Skin cancer is the most common cancer in the existing world constituting one-third of the cancer cases. Benign skin cancers are not fatal, can be cured with proper medication. But it is not the same as the malignant skin cancers. In the case of malignant melanoma, in its peak stage, the maximum life expectancy is less than or equal to 5 years. But, it can be cured if detected in early stages. Though there are numerous clinical procedures, the accuracy of diagnosis falls between 49% to 81% and is time-consuming. So, dermoscopy has been brought into the picture. It helped in increasing the accuracy of diagnosis but could not demolish the error-prone behaviour. A quick and less error-prone solution is needed to diagnose this majorly growing skin cancer. This project deals with the usage of deep learning in skin lesion classification. In this project, an automated model for skin lesion classification using dermoscopic images has been developed with CNN(Convolution Neural Networks) as a training model. Convolution neural networks are known for capturing features of an image. So, they are preferred in analyzing medical images to find the characteristics that drive the model towards success. Techniques like data augmentation for tackling class imbalance, segmentation for focusing on the region of interest and 10-fold cross-validation to make the model robust have been brought into the picture. This project also includes usage of certain preprocessing techniques like brightening the images using piece-wise linear transformation function, grayscale conversion of the image, resize the image. This project throws a set of valuable insights on how the accuracy of the model hikes with the bringing of new input strategies, preprocessing techniques. The best accuracy this model could achieve is 0.886.

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.