The submission opens October 1 and closes on November 12, 2025
New technologies are helping vulnerable communities produce maps that integrate topographical, infrastructural, seasonal, and real-time data — an essential tool for many humanitarian endeavors.
Amazon vice president and distinguished engineer Marc Brooker explains how agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
A new dataset with over 238,000 images challenges and advances the state of the art in visual defect detection for complex retail applications.
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Amazon's unique approach to research is characterized by its customer-obsessed science philosophy, which drives innovation in artificial intelligence, machine learning, and other cutting-edge technologies.
Research areas
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January 26, 20262 min readLeveraging existing environment simulators and reward functions based on verifiable ground truth boosts task success rate, even with small models and small training datasets. -
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September 23, 20256 min readMachine learning
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Featured news
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
The collaboration will advance research in generative AI, robotics, natural language processing and cloud computing while fostering innovation in foundational and emerging technologies.
University teams battle to harden and hack AI coding assistants in head-to-head tournament
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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.
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2024In this work, we propose a novel sequence-discriminative training criterion for automatic speech recognition (ASR) based on the Conformer Transducer. Inspired by the large-margin classifier framework, we separate the “good” and the “bad” hypotheses in an N-best list produced from a pre-trained transducer model by a margin (τ ), hence the term, Max-Margin Transducer (MMT) loss. It is observed that fine-tuning
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2024Speech codec enhancement methods are designed to remove distortions added by speech codecs. While classical methods are very low in complexity and add zero delay, their effectiveness is rather limited. Compared to that, DNN-based methods deliver higher quality but they are typically high in complexity and/or require delay. The recently proposed Linear Adaptive Coding Enhancer (LACE) addresses this problem
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2024The traditional cascading Entity Resolution (ER) pipeline suffers from propagated errors from upstream tasks. We address this issue by formulating a new end-to-end (E2E) ER problem, Signal-to-Entity (S2E), resolving query entity mentions to actionable entities in textual catalogs directly from audio queries instead of audio transcriptions in raw or parsed format. Additionally, we extend the E2E Spoken Language
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2024Automated speaker identification (SID) is a crucial step for the per-sonalization of a wide range of speech-enabled services. Typical SID systems use a symmetric enrollment-verification framework with a single model to derive embeddings both offline for voice profiles extracted from enrollment utterances, and online from runtime utter-ances. Due to the distinct circumstances of enrollment and runtime, such
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ECIR 20242024Off-policy evaluation (OPE) methods allow us to compute the expected reward of a policy by using the logged data collected by a different policy. However, when the number of actions is large, or certain actions are under-explored by the logging policy, existing estimators based on inverse-propensity scoring (IPS) can have a high or even infinite variance. Saito and Joachims [13] propose marginalized IPS
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Gretchen Ertl
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.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
