Neural architecture search (NAS) methods have been proposed to release human experts from tedious architecture engineering. However, most current methods are constrained in small-scale search due to the issue of computational resources. Meanwhile, directly applying architectures searched on small datasets ...
graph-based entity/relation embedding, and 3) a new formulation of neural architecture search as a graph topology optimization problem, with simple yet powerful algorithms that automatically discover high-performing convolutional neural architectures on image recognition benchmarks, and reduce the ...
Graph Neural Networks (GNN): this recent and yet not-widely adopted deep learning methodology (mostly applied to 2D mesh data in aerodynamics-related literature [22,23]) allows to deal also with highly heterogeneous and unstructured 3D data by training directly on the graph made up of nodes an...
The platform is realized in Java using Spring Boot (https://spring.io/projects/spring-boot), while its architecture is realized on top of Apache Ignite (https://ignite.apache.org/). The platform's architecture, therefore, is distributed by design. It follows the manager/worker architecture st...
The success of training computer-vision models heavily relies on the support of large-scale, real-world images with annotations. Yet such an annotation-ready dataset is difficult to curate in pathology due to the privacy protection and excessive annotati
These parameter values were each selected using 1-D grid search. All other training parameters were set to their default values30. The stride of the neural network was 8, yielding output heatmaps and refinement maps 8-10 times smaller in each dimension than the input images (for example, ...
Graph Databases FlockDB: Distributed Graph Database at Twitter TAO: Distributed Data Store for the Social Graph at Facebook Akutan: Distributed Knowledge Graph Store at eBay Time Series Databases Beringei: High-performance Time Series Storage Engine at Facebook ...
In recent years, some computer vision algorithms such as SIFT (Scale Invariant Feature Transform) have been employed in image similarity match to perform image-based search applications. However, with the increasing scale of image databases, centralized image retrieval system no longer provide adequate...
This deep neural network approach leads to more detailed “deconvolution” of the target bulk data into precise single-cell level measurements. Consequently, with only the budget for bulk sequencing and single-cell sequencing of representatives, scSemiProfiler outputs single-cell data for all samples ...
The framework of Elastic Architecture Transfer for Neural Architecture Search (EAT-NAS). We firstly search for the basic model on the small-scale task and then search on the large-scale task with the basic model as the initialization seed of the new population. Requirements mxnet 1.3.1 python...