Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner.
MORE adopts the same variational ansatz as binary classifiers while performing multi-classification by fully utilizing the quantum information of a single readout qubit.
In a number of practical scenarios, VFL is more relevant than HFL as different companies (e. g., bank and retailer) hold different features (e. g., credit history and shopping history) for the same set of customers.
The quantum feature extractors in the SQNN system are independent of each other, so one can flexibly use quantum devices of varying sizes, with larger quantum devices extracting more local features.
LAWS is a combinatorial optimization strategy taking advantage of model parameter initialization and fast convergence of QNG.
A high-resolution network exhibits remarkable capability in extracting multi-scale features for human pose estimation, but fails to capture long-range interactions between joints and has high computational complexity.
Ranked #34 on Pose Estimation on COCO test-dev
Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time.
Moreover, we provide new variant of Adam-Type algorithm, namely AdamAL which can naturally mitigate the non-convergence issue of Adam and improve its performance.
We then compare the performance of the proposed method with Kronecker compressive sensing (KCS) and multi way compressive sensing (MWCS).