Changyou Chen

Jun 7, 2024 | amazon.science | Qin Zhang |Changyou Chen

In recent years, the fields of natural-language processing and computational linguistics, which were revolutionized a decade ago by deep learning, were revolutionized again by large language models (LLMs). Unsurprisingly, work involving LLMs, either as a subject of inquiry themselves or as tools for other natural-language-processing applications, predominates at this year’s meeting of the North American Chapter of the Association for Computational Linguistics (NAACL).

Feb 6, 2024 | dx.doi.org | Changyou Chen |Haitao Chen |Pingping Wang |Xue Wang

Download Hi-Res ImageDownload to MS-PowerPointCite This:ACS Chem. Biol. 2024, XXXX, XXX Terms & Conditions Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses).

Dec 20, 2023 | amazon.science | Larry Hardesty |Changyou Chen |Raffay Hamid

Last month, at its annual re:Invent developers’ conference, Amazon Web Services (AWS) announced the release of two new additions to its Titan family of foundation models, both of which translate between text and images. With Amazon Titan Multimodal Embeddings, now available through Amazon Bedrock, customers can upload their own sets of images and then search them using text, related images, or both.

Nov 16, 2023 | amazon.science | Michael Kearns |Aaron Thomas Roth |Changyou Chen |Daniel Zhang

When we first joined AWS AI/ML as Amazon Scholars over three years ago, we had already been doing scientific research in the area now known as responsible AI for a while. We had authored a number of papers proposing mathematical definitions of fairness and machine learning (ML) training algorithms enforcing them, as well as methods for ensuring strong notions of privacy in trained models.

Oct 6, 2023 | amazon.science | Gunnar Sigurdsson |Raffay Hamid |Changyou Chen

Remote-object grounding is the task of automatically determining where in the local environment to find an object specified in natural language.