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.
Customer-obsessed science
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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NeurIPS 2023 Workshop on Robot Learning2023Offline meta-reinforcement learning (OMRL) aims to generalize an agent’s knowledge from training tasks with offline data to a new unknown RL task with few demonstration trajectories. This paper proposes T3GDT: Three-tier tokens to Guide Decision Transformer for OMRL. First, our approach learns a global token from its demonstrations to summarize a RL task’s transition dynamic and reward pattern. This global
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NeurIPS 20232023We present a framework for transfer learning that efficiently adapts a large basemodel by learning lightweight cross-attention modules attached to its intermediate activations. We name our approach InCA (Introspective-Cross-Attention) and show that it can efficiently survey a network’s representations and identify strong performing adapter models for a downstream task. During training, InCA enables training
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KDD 2024, NeurIPS 2023 Workshop on Distribution Shifts (DistShifts)2023Pre-trained language models (PLMs) have seen tremendous success in text classification (TC) problems in the context of Natural Language Processing (NLP). In many real-world text classification tasks, the class definitions being learned do not remain constant but rather change with time - this is known as concept shift. Most techniques for handling concept shift rely on retraining the old classifiers with
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NeurIPS 20232023Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the unprecedented increase in spatiotemporal Earth observation data has enabled data-driven forecasting models using deep learning techniques. These models have shown promise for diverse Earth system forecasting tasks. However, they
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NeurIPS 20232023Ordinal classification (OC), i.e., labeling instances along classes with a natural ordering, is common in multiple applications such as disease severity labeling and size or budget based recommendations. Often in practical scenarios, it is desirable to obtain a small set of likely classes with a guaranteed high chance of including the true class. Recent works on conformal prediction (CP) address this problem
Collaborations
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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.
