In a keynote address at the latest Amazon Machine Learning Conference, Amazon academic research consultant, Stanford professor, and recent Nobel laureate Guido Imbens offered insights on the estimation of causal effects in “panel data” settings.
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The Amazon AGI SF Lab will focus on developing new foundational capabilities for enabling useful AI agents.
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January 14, 2025Key exchange protocols and authentication mechanisms solve distinct problems and must be integrated in a secure communication system. -
December 24, 2024
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December 24, 2024
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Led by David Luan and Pieter Abbeel, the lab will focus on developing new foundational capabilities for enabling useful AI agents.
The company's new state-of-the-art foundation models deliver frontier intelligence and industry-leading price performance.
Caltech professor and Amazon Scholar John Preskill wins Bell Prize for applying both classical and quantum computing to the problem of learning from quantum experiments.
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Research on neural networks for time series has mostly focused on developing models that learn patterns about the target signal without the use of additional auxiliary or exogenous information. In applications such as selling products on a marketplace, the target signal is influenced by these variables, and leveraging exogenous variables is important. In particular, knowing that a product would go into
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2024We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos. Leveraging the reasoning capabilities and world knowledge of large language models, and augmented by the Segment Anything Model, VideoLISA generates temporally consistent segmentation masks in videos based on language instructions. Existing image-based
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Reinforcement Learning (RL) has achieved state-of-the-art performance in station-ary environments with effective simulators. However, lifelong and open-world RL applications, such as robotics, stock trading, and recommendation systems, change over time in adversarial ways. Non-stationary environments pose challenges for RL agents due to constant distribution shifts from the training data, leading to deteriorating
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2024Visual-Language Alignment (VLA) has gained a lot of attention since CLIP’s groundbreaking work. Although CLIP performs well, the typical direct latent feature alignment lacks clarity in its representation and similarity scores. On the other hand, lexical representation, a vector whose element represents the similarity between the sample and a word from the vocabulary, is a natural sparse representation
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IEEE Big Data 20242024Getting large language models (LLMs) to perform well on the downstream tasks requires pre-training over trillions of tokens. This typically demands a large number of powerful computational devices in addition to a stable distributed training framework to accelerate the training. The growing number of applications leveraging AI/ML led to a scarcity of the expensive conventional accelerators (such as GPUs
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The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
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A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and LLM coding security.
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We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
