Customer-obsessed science
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September 30, 2024From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money.
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September 19, 2024“Agentic workflows” that use multiple, fine-tuned smaller LLMs — rather than one large one — can improve efficiency.
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September 16, 2024A position paper presented at ACL proposes a framework for more-accurate human evaluation of LLMs.
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September 29 - October 4, 2024
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October 21 - 25, 2024
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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.
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ACL 20232023Methods to generate text from structured data have advanced significantly in recent years, primarily due to fine-tuning of pre-trained lan-guage models on large datasets. However, such models can fail to produce output faithful to the input data, particularly on out-of-domain data. Sufficient annotated data is often not avail-able for specific domains, leading us to seek an unsupervised approach to improve
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HCOMP 20232023Video object tracking annotation tasks are a form of complex data labeling that is inherently tedious and time-consuming. Prior studies of these tasks focus primarily on quality of the provided data, leaving much to be learned about how the data was generated and the factors that influenced how it was generated. In this paper, we take steps toward this goal by examining how human annotators spend their
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ICCV 20232023We investigate compositional structures in data embeddings from pre-trained vision-language models (VLMs). Traditionally, compositionality has been associated with algebraic operations on embeddings of words from a preexisting vocabulary. In contrast, we seek to approximate representations from an encoder as combinations of a smaller set of vectors in the embedding space. These vectors can be seen as “ideal
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IEEE CDC 20232023Robotic sortation centers use mobile robots to sort packages by their destinations. The destination-to-sortlocation (chute) mapping can significantly impact the volume of packages that can be sorted by the sortation floor. In this work, we propose a multi-agent reinforcement learning method to solve large-scale chute mapping problems with hundreds of agents (the destinations). To address the exponential
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Winter Simulation Conference 20232023Developing a comprehensive model is a practical approach for gaining insight into and analyzing complex systems such as transportation yards. Following this approach, we have developed a data-driven agentbased model for transportation yards at Amazon which captures the features and processes of yard operations. By simulating different scenarios and using simulation performance indicators such as yard/parking
Resources
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We look for talent from around the world for applied scientists, data scientists, economists, research scientists, scholars, academics, PhDs, and interns.
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We collaborate with leading academic organizations to drive innovation and to ensure that research is creating solutions whose benefits are shared broadly.
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Learn more about the awards and recognitions that Amazon researches from around the world have been honored with during their tenure.