Deep Kernel Machines

深度核机（dkm）代表了一种新的核方法，它与神经网络（nn）在学习有效数据表示的能力方面有相似之处。然而，与神经网络不同的是，dkm完全通过核获取这些表示，绕过了不可思议的权重矩阵。这种独特的特性为深入了解深度学习模型背后的机制提供了机会。最近的研究表明，在CIFAR-10等图像分类任务上，卷积dkm可以达到与ResNets等模型相当的性能。这表明dkm作为神经网络的一种实用替代方案的潜力。本研究项目旨在通过将dkm应用于不同的数据集领域来扩大dkm的范围，包括通过图深核机的图和通过深核转换器的序列。本研究的目标是将dkm细化为一种实用的方法，并通过基于核的学习的视角阐明深度模型工作的新视角。该项目属于EPSRC人工智能和机器人/数学科学研究领域

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