Customer-obsessed science
Research areas
-
July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
-
July 9, 202610 min read
-
Featured news
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.
-
KDD 2021 Workshop on Pretraining: Algorithms, Architectures, and Applications2021Few-shot learning techniques rely on generalizations of a base model trained on a large training set in order to transfer learn to more specialized tasks. Such techniques extend unsupervised learning models by allowing for fine-tuning a general model to a domain of interest using a relatively low number of training samples. This is especially important for applications where data may be non-homogeneous
-
EMBC 20212021In this work, we propose to use a deep learning framework for decoding the electroencephalogram (EEG) signals of human brain activities. More specifically, we learn an end-to-end model that recognizes natural images or motor imagery by the EEG data that is collected from the corresponding human neural activities. In order to capture the temporal information encoded in the long EEG sequences, we first employ
-
ESEC/FSE 20212021We present Rapid, an industrial-strength analysis developed at AWS that aims to help developers by providing automatic, fast and actionable feedback about correct usage of cloud-service APIs. Rapid’s design is based on the insight that cloud service APIs are structured around short-lived request- and response-objects whose usage patterns can be specified as value-dependent type-state automata and be verified
-
IWSLT 20212021Sub-word segmentation is currently a standard tool for training neural machine translation (MT) systems and other NLP tasks. The goal is to split words (both in the source and target languages) into smaller units which then constitute the input and output vocabularies of the MT system. The aim of reducing the size of the input and output vocabularies is to increase the generalization capabilities of the
-
ECML-PKDD 20212021In this work, we develop an optimal transport (OT) based framework to select informative prototypical examples that best represent a given target dataset. Summarizing a given target dataset via representative examples is an important problem in several machine learning applications where human understanding of the learning models and underlying data distribution is essential for decision making. We model
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all
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.
