Goto

Collaborating Authors

Results


ModelArts 3.0: a Arue AI Accelerator

#artificialintelligence

HUAWEI CLOUD's Enterprise Intelligence (EI) has achieved strong results in numerous industry competitions and evaluations. HUAWEI CLOUD has invested heavily in basic research AI in three domains: computer vision, speech and semantics, and decision optimization. To help AI empower all industries, the ModelArts enabling platform supports plug-and-play deployment of HUAWEI CLOUD's research results in areas such as automatic machine learning, small sample learning, federated learning, and pre-training models. In the area of perception, HUAWEI CLOUD continues to be an industry-leader in ImageNet large-scale image classification, WebVision large-scale network image classification, MS-COCO two-dimensional object detection, nuScenes three-dimensional object detection, and visual pre-training model verification, including downstream classification, detection, and segmentation. Perception models driven by ModelArts have been widely used in sectors such as medical image analysis, oil and gas exploration, and fault detection in manufacturing. In cognition, HUAWEI CLOUD integrates industry data based on its expertise in semantic analysis and knowledge graphs.


Hyperbox based machine learning algorithms: A comprehensive survey

arXiv.org Machine Learning

With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially. Along with this trend, machine learning algorithms have been formed and evolved continuously to discover new information and knowledge from different data sources. Learning algorithms using hyperboxes as fundamental representational and building blocks are a branch of machine learning methods. These algorithms have enormous potential for high scalability and online adaptation of predictors built using hyperbox data representations to the dynamically changing environments and streaming data. This paper aims to give a comprehensive survey of literature on hyperbox-based machine learning models. In general, according to the architecture and characteristic features of the resulting models, the existing hyperbox-based learning algorithms may be grouped into three major categories: fuzzy min-max neural networks, hyperbox-based hybrid models, and other algorithms based on hyperbox representation. Within each of these groups, this paper shows a brief description of the structure of models, associated learning algorithms, and an analysis of their advantages and drawbacks. Main applications of these hyperbox-based models to the real-world problems are also described in this paper. Finally, we discuss some open problems and identify potential future research directions in this field.