We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply. As a wrapper to TensorFlow and many other libraries (e.g., transformers, scikit-learn, stellargraph), it is designed to make sophisticated, state-of-the-art machine learning models simple to build, train, inspect, and apply by both beginners and experienced practitioners. Featuring modules that support text data (e.g., text classification, sequence tagging, open-domain question-answering), vision data (e.g., image classification), graph data (e.g., node classification, link prediction), and tabular data, ktrain presents a simple unified interface enabling one to quickly solve a wide range of tasks in as little as three or four "commands" or lines of code.
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
ServiceNow Research（ServiceNow 研究所）；University of British Columbia（不列颠哥伦比亚大学）；University of Toronto（多伦多大学）；University of Montreal（蒙特利尔大学）；Mila（Mila 研究所）；CIFAR AI Chair（CIFAR 人工智能主席职位）；Polytechnique Montréal（蒙特利尔理工学院）
RAGraph: A General Retrieval-Augmented Graph Learning Framework
Key Laboratory of High Confidence Software Technologies (Peking University)（高可信软件技术重点实验室（北京大学））；School of Computer Science, Peking University（北京大学计算机学院）；University of Electronic Science and Technology of China（电子科技大学）；Center on Frontiers of Computing Studies, Peking University（北京大学前沿计算研究中心）；Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China（嘉兴南湖实验室大数据技术研究中心）；Peking University Information Technology Institute, Tianjin Binhai（北京大学信息技术学院（天津滨海））
