Machine learning (ML) algorithms are showing a growing trend in helping the
scientific communities across different disciplines and institutions to address
large and diverse data problems. However, many available ML tools are
programmatically demanding and computationally costly. The MLExchange project
aims to build a collaborative platform equipped with enabling tools that allow
scientists and facility users who do not have a profound ML background to use
ML and computational resources in scientific discovery. At the high level, we
are targeting a full user experience where managing and exchanging ML
algorithms, workflows, and data are readily available through web applications.
Since each component is an independent container, the whole platform or its
individual service(s) can be easily deployed at servers of different scales,
ranging from a personal device (laptop, smart phone, etc.) to high performance
clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange
renders flexible using scenarios -- users could either access the services and
resources from a remote server or run the whole platform or its individual
service(s) within their local network.

MLExchange 项目旨在通过构建协作平台来帮助不具备深度机器学习背景的科学家和实验用户使用机器学习和计算资源，旨在为科学领域中不同领域和机构的科学社区解决大规模和多样化的数据问题。

MLExchange：一个基于 Web 的平台，为科学研究提供可交换的机器学习工作流程

MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies

Isolated silos of scientific research and the growing challenge of
information overload limit awareness across the literature and hinder
innovation. Algorithmic curation and recommendation, which often prioritize
relevance, can further reinforce these informational "filter bubbles." In
response, we describe Bridger, a system for facilitating discovery of scholars
and their work. We construct a faceted representation of authors with
information gleaned from their papers and inferred author personas, and use it
to develop an approach that locates commonalities and contrasts between
scientists to balance relevance and novelty. In studies with computer science
researchers, this approach helps users discover authors considered useful for
generating novel research directions. We also demonstrate an approach for
displaying information about authors, boosting the ability to understand the
work of new, unfamiliar scholars. Our analysis reveals that Bridger connects
authors who have different citation profiles and publish in different venues,
raising the prospect of bridging diverse scientific communities.

通过 Bridger 系统的算法筛选、构建学者特征，实现了学者发现与相关工作信息检索，拓宽了学术领域内的沟通与合作，对推动科学创新和学科发展有重要意义。