Customer-obsessed science
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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 10, 2024Automated reasoning and optimizations specific to CPU microarchitectures improve both performance and assurance of correct implementation.
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September 29 - October 4, 2024
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November 12 - 16, 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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NeurIPS 20232023Membership inference attacks are designed to determine, using black-box access to trained models, whether a particular example was used in training or not. Membership inference can be formalized as a hypothesis-testing problem. The most effective existing attacks estimate the distribution of some test statistic (usually the model’s confidence on the true label) on points that were (and were not) used in
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NeurIPS 20232023We focus on the task of approximating the optimal value function in deep reinforcement learning. This iterative process is comprised of solving a sequence of optimization problems where the loss function changes per iteration. The common approach to solving this sequence of problems is to employ modern variants of the stochastic gradient descent algorithm such as Adam. These optimizers maintain their own
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Robotic Computing 20232023Home robots operate in diverse and dynamic environments, delivering a range of functions that enhance utility. Many of these functions span extended periods, from weeks to months, typically improving through observations and interactions. Efficient development and validation of these functions necessitate simulations that can run faster than real time. However, many current robot simulators focus on high-fidelity
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ACM Transactions on Architecture and Code Optimization2023Low-precision computation has emerged as one of the most effective techniques for accelerating convolutional neural networks and has garnered widespread support on modern hardware. Despite its effectiveness in accelerating convolutional neural networks, low-precision computation has not been commonly applied to fast convolutions, such as the Winograd algorithm, due to numerical issues. In this paper, we
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NeurIPS 20232023Spoken language understanding (SLU) systems often exhibit suboptimal performance in processing atypical speech, typically caused by neurological conditions and motor impairments. Recent advancements in Text-to-Speech (TTS) synthesis-based augmentation for more fair SLU have struggled to accurately capture the unique vocal characteristics of atypical speakers, largely due to insufficient data. To address
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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.