… support from nonlinear kernel functions; thus, … kernel function fails to correctly characterize
the difference between intraclass similarity and interclass similarity. In this paper, a new kernel …

… the parameters of both the network and the kernel can be learned jointly using SM. Following
the … As done for SSM, we calculate the tractable solution of the kernel method when training …

… efficient matching operator cross-correlation and its variants. Besides the remarkable success,
it is important to note that the heuristic matching … (eg, convolution kernels) learned on the …

… (9) applies different 1×1 convolution kernels on spatial … a global 1 × 1 convolution kernel.
The methods above extracts … also present inferior retrieval efficiency compared with APNet. …

… graph matching models outperforms the embedding models across all settings and propagation
steps. Additionally, we have tried the WL kernel … Efficient matching and indexing of graph …

… rise to a balance-variance Pareto-efficient version of optimal matching and to a method that
… In this section we develop kernel optimal matching (KOM) methods, which are given by …

… The kernel exponential family is a rich class of distributions, which can be … efficiently and
with statistical guarantees by score matching. Being required to choose a priori a simple kernel …

… -efficiency and expressive power while provably maintaining the classification accuracy of
the original classifier. Mix-n-Match … Therefore, we propose an alternative data-efficient kernel …

… We show, perhaps surprisingly, that our framework encompasses this approach, but deep
kernels allow for more general model classes which can use the data more efficiently. We also …

… • iii) Based on the results, we propose three high-quality and efficient matching systems,
which perform on par with the powerful CODE [23] system but are several orders of magnitude …