Customer-obsessed science
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February 14, 2023A diversity of outputs ensures that style transfer model can satisfy any user’s tastes.
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February 06, 2023Methods for controlling the outputs of large generative models and integrating symbolic reasoning with machine learning are among the conference’s hot topics.
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February 02, 2023Research on “super-Grover” optimization, quantum algorithms for topological data analysis, and simulation of physical systems displays the range of Amazon’s interests in quantum computing.
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February 27 - March 3, 2023
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April 30 - May 4, 2023
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May 1 - 5, 2023
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March 01, 2023New fellows include PhD candidates in operations research and computer science.
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February 24, 2023Session focused on tips and tools that can help customers reduce the carbon footprint of artificial intelligence and machine learning workloads.
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February 22, 2023Researchers honored for their contributions to the scientific community.
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February 09, 2023The collaboration includes Amazon funding for faculty research projects, with an initial focus on machine learning and natural-language processing.
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IEEE Transactions on Pattern Analysis and Machine Intelligence2023The architecture of transformers, which recently witness booming applications in vision tasks, has pivoted against the widespread convolutional paradigm. Relying on the tokenization process that splits inputs into multiple tokens, transformers are capable of extracting their pairwise relationships using self-attention. While being the stemming building block of transformers, what makes for a good tokenizer
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EACL 20232023Despite significant progress in understanding and improving faithfulness in abstractive summarization, the question of how decoding strategies affect faithfulness is less studied. We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive summarization. We find a consistent trend where beam search with large beam sizes produces
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EACL 20232023Temporal concept drift refers to the problem of data changing over time. In NLP, that would entail that language (e.g. new expressions, meaning shifts) and factual knowledge (e.g. new concepts, updated facts) evolve over time. Focusing on the latter, we benchmark 11 pretrained masked language models (MLMs) on a series of tests designed to evaluate the effect of temporal concept drift, as it is crucial that
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EACL 20232023Neural models for abstractive summarization tend to generate output that is fluent and wellformed but lacks semantic faithfulness, or factuality, with respect to the input documents. In this paper, we analyze the tradeoff between abstractiveness and factuality of generated summaries across multiple datasets and models, using extensive human evaluations of factuality. In our analysis, we visualize the rates
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The availability of large amounts of informative data is crucial for successful machine learning. However, in domains with sensitive information, the release of high-utility data which protects the privacy of individuals has proven challenging. Despite progress in differential privacy and generative modeling for privacy-preserving data release in the literature, only a few approaches optimize for machine
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January 12, 2023The collaboration, housed in the College of Engineering, includes funds for faculty research projects, with an initial focus on AI, robotics, and operations research.
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December 21, 2022Fifth challenge adds new elements and features four new competitors for the $1 million research grant.
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December 16, 2022University of Wisconsin-Madison associate professor and ARA recipient has authored a series of pioneering papers on real-time object instance segmentation.
Working at Amazon
View allMeet the people driving the innovation essential to being the world’s most customer-centric company.
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February 28, 2023How the former astrobiology professor is charting new territory as a scientist for Amazon Flex.
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February 08, 2023How her background helps her manage a team charged with assisting internal partners to answer questions about the economic impacts of their decisions.
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February 07, 2023Parmida Beigi, an Amazon senior research scientist, shares a lifetime worth of experience, and uses her skills to help others grow into machine learning career paths.