Runtime optimizations and a thorough evaluation process driven by agentic AI help ensure that models written in PyTorch will execute efficiently and reliably on Trainium systems.
Expanding the mix of synthetic data used to train a tabular foundation model enables it to adapt to new tables without new models and lead the single-model field on the TabArena benchmark.
Using the Neuron Kernel Interface, a Reactor–AWS collaboration tackled the dynamic shapes, memory access patterns, and cache management that make real-time autoregressive diffusion hard—building techniques that generalize across models.
Three new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.
New research indicates that AI agents learn compressible models of data, which don’t have enough space to enable memorization.
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November 11, 20265 min readStatic analysis of deployed networks helps reduce the risk that sensitive data leaks across partitions, supporting the security and reliability of global-scale networking. -
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October 20, 20263 min read
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The collaboration aims to advance research while broadening participation and translating discovery into real-world solutions.
Submissions open October 1 and close on November 11, 2026
The collaboration will support research in robotics, industrial and systems engineering, and aerospace technology while fostering innovation in foundational and emerging technologies.
The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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CVPR 2019 Robotic Vision Probabilistic Object Detection Challenge2019Vision is an integral part of many robotic systems, and especially so when a robot must interact with its environment. In such cases, decisions made based on erroneous visual detections can have disastrous consequences. Hence, being able to accurately measure the uncertainty associated with visual information is highly important for making informed decisions. However, this uncertainty is often not captured
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Interspeech 20192019We present a hybrid approach for scaling distributed training of neural networks by combining Gradient Threshold Com-pression (GTC) algorithm - a variant of stochastic gradient de-scent (SGD) - which compresses gradients with thresholding and quantization techniques and Blockwise Model Update Filtering(BMUF) algorithm - a variant of model averaging (MA). In this proposed method we divide total number of
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ICCV 2019 Workshop on Computer Vision for Fashion, Art and Design2019The emergence of online influencers, the explosion of video content, and the massive amount of movie collections have served as an advertising vehicle for the fashion industry. This trend has created the need for automated methods that recognize people’s outfit in such image and video collections. However, existing computer vision solutions for fashion recognition require an enormous amount of labeled data
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MMSP 20192019With the massive growth of Internet video streaming, it is critical to accurately measure video quality subjectively and objectively, especially HD and UHD video which is bandwidth-intensive. We summarize the creation of a database of 200 clips, with 20 unique sources tested across a variety of devices. By classifying the test videos into 2 distinct quality regions SD and HD, we show that the high correlation
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CVPR 20192019Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their
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.
