Deep Kernel Machines

深层内核机器

• 批准号: 2740530
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2740530
• 关键词: Deep; Kernel; Machines

Deep Kernel Machines (DKMs) represent a novel category of kernel methods that share similarities with neural networks (NNs) in their ability to learn effective data representations. However, unlike NNs, DKMs acquire these representations exclusively through kernels, bypassing inscrutable weight matrices. This unique characteristic offers an opportunity to gain insights into the mechanisms underlying deep learning models. Recent studies have demonstrated that convolutional DKMs can achieve competitive performance, comparable to models like ResNets, on image classification tasks such as CIFAR-10. This suggests the potential of DKMs as a practical alternative to neural networks. This research project aims to broaden the scope of DKMs by applying them to diverse dataset domains, including graphs through the Graph Deep Kernel Machine and sequences via Deep Kernel Transformers. The objectives of this research are to refine DKMs into a practical methodology and to illuminate novel perspectives on the workings of deep models through the lens of kernel-based learning. This project falls within the EPSRC artificial intelligence and robotics/mathematical sciences research area

深内核计算机（DKM）代表了内核方法​​的新类别，它们在学习有效数据表示的能力方面与神经网络（NNS）共享相似之处。但是，与NNS不同，DKMS仅通过内核获取这些表示，绕过了难以理解的重量矩阵。这种独特的特征为深入了解深度学习模型的机制提供了机会。最近的研究表明，卷积DKM可以在图像分类任务（例如CIFAR-10）上实现竞争性能，与Resnets等模型相当。这表明DKM作为神经网络的实际替代方案的潜力。该研究项目旨在通过将DKM的范围应用于各种数据集域，包括通过图形深内核机和通过深内核变压器序列的图形。这项研究的目标是将DKM改进实际方法，并通过基于内核的学习镜头来阐明对深层模型的工作的新观点。该项目属于EPSRC人工智能和机器人技术/数学科学研究领域