Customer-obsessed science


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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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January 23, 2023Two Alexa AI papers present novel methodologies that use vision and language understanding to improve embodied task completion in simulated environments.
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January 20, 2023Prompt engineering enables researchers to generate customized training examples for lightweight “student” models.
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February 4 - 10, 2023
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February 7 - 14, 2023
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February 27 - March 3, 2023
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January 30, 2023Awardees, who represent 24 universities in seven countries, have access to Amazon public datasets, along with AWS AI/ML services and tools.
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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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January 09, 2023Mehrdad Mahdjoubi, founder and CEO of Alexa Fund portfolio company, explains ‘no compromise’ approach to saving resources without sacrificing user experience.
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January 05, 2023How an AWS customer uses Lookout for Vision to build custom computer vision models to automate quality inspection and detect defects.
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2023What are quantum computers good at? The earliest and still-strongest candidates are factoring and simulation of quantum systems. While the complexity of factoring is relatively well understood, that of quantum simulation at low energies is considerably less clear-cut. For starters, we know from complexity theory that the ground energy problem for local Hamiltonians is QMA-hard in the worst case, and thus
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2023Topological invariants of a dataset, such as the number of holes that survive from one length scale to another (persistent Betti numbers) can be used to analyse and classify data in machine learning applications. We present an improved quantum algorithm for computing persistent Betti numbers, and provide an end-to-end complexity analysis. Our approach provides large polynomial time improvements, and an
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2023Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries. However, recent studies reveal that text-to-SQL models are vulnerable to task-specific perturbations. Previous curated robustness test sets usually focus on individual phenomena. In this paper, we propose a comprehensive robustness benchmark based on Spider, a cross-domain text-to-SQL
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ASPLOS 20232023As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide efficient model serving. However, it is challenging to develop efficient tensor programs for deep learning operators due to the high complexity of modern accelerators (e.g., NVIDIA GPUs and Google TPUs) and the rapidly growing number
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ALT 20232023We study the problem of Reinforcement Learning (RL) with linear function approximation, i.e. assuming the optimal action-value function is linear in a known d-dimensional feature mapping. Unfortunately, however, based on only this assumption, the worst case sample complexity has been shown to be exponential, even under a generative model. Instead of making further assumptions on the MDP or value functions
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December 23, 2022Program focuses on diversifying tech-industry talent.
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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
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January 31, 2023Mahdieh Allahviranloo is using her expertise in transportation engineering to assist last-mile delivery operations at Amazon.
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December 20, 2022Ariadna Sanchez, a scientist who works in polyglot text to speech, draws on her musical background to help find novel solutions.
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December 13, 2022Amazon Machine Learning Fellow Jiao Sun works on strategies to control text generation.