In the proposed ensemble averaging method, multiple models are independently trained and model predictions are averaged at each time step.
Matrix multiplication is the bedrock in Deep Learning inference application.
To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client.
Consequently, a trained DNN defines a predictive model for the underlying unknown PDE over structureless grids.
While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion.
As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants.
To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances.
The overdetermination of the mathematical problem underlying ptychography is reduced by a host of experimentally more desirable settings.
When an existing coarse model is not available, we present numerical strategies for fast creation of coarse models, to be used in conjunction with the generalized ResNet.
To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning.
In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology images (BACH).