Customer-obsessed science
-
September 19, 2024“Agentic workflows” that use multiple, fine-tuned smaller LLMs — rather than one large one — can improve efficiency.
-
September 16, 2024A position paper presented at ACL proposes a framework for more-accurate human evaluation of LLMs.
-
September 10, 2024Automated reasoning and optimizations specific to CPU microarchitectures improve both performance and assurance of correct implementation.
-
-
September 29 - October 4, 2024
-
November 12 - 16, 2024
-
September 25, 2024
Now open until November 6, Amazon Research Awards will be seeking proposals in the following research areas: AI for Information Security, Automated Reasoning, AWS AI, AWS Cryptography, and Sustainability.
-
2024Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this work, we leverage Multiple Instance Learning (MIL) to overcome this issue, and pro-pose a new framework called MILLET: Multiple Instance Learning for Locally Explainable Time series classification. We apply MILLET to existing deep learn-ing TSC models
-
2024Cloud data warehouses are today’s standard for analytical query processing. Multiple cloud vendors offer state-of-the-art systems, such as Amazon Redshift. We have observed that customer work-loads experience highly repetitive query patterns, i.e., users and systems frequently send the same queries. In order to improve query performance on these queries, most systems rely on techniques like result caches
-
2024Training a supervised news summarization model requires large amounts of high-quality training data consisting of news articles paired with reference summaries. However, obtaining such data is costly, and existing datasets contain considerable amount of noise. We present a new large-scale and high-quality dataset for supervised abstractive news summarization containing 1.3 million training samples, which
-
ESWC 20242024We present an approach to represent composite values (lists and maps, in particular) as literals in RDF data, and to extend SPARQL with features related to such literals. These extensions include an aggregation function to produce these composite values, functions to operate on these composite values in expressions, and a new operator to unfold such composite values into their individual components. As
-
SIOP 20242024Correlating assessment scores with performance in role (PIR) metrics provides a powerful form of validation evidence, but is complicated by the absence of PIR metrics for applicants who were not hired. Traditional range restriction perspectives state that the problem is a lack of PIR metrics for low assessment scores, and typical corrections make strong assumptions about how the relationship among incumbents
Resources
-
We look for talent from around the world for applied scientists, data scientists, economists, research scientists, scholars, academics, PhDs, and interns.
-
We collaborate with leading academic organizations to drive innovation and to ensure that research is creating solutions whose benefits are shared broadly.
-
Learn more about the awards and recognitions that Amazon researches from around the world have been honored with during their tenure.