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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
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July 9, 202610 min read
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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 2023 Workshop on Multimodal Content Moderation (MMCM)2023Online advertisement industry aims to build a preference for a product over its competitors by making consumers aware of the product at internet scale. However, the ads that violate the applicable laws and location specific regulations can have serious business impact with legal implications. At the same time, customers are at risk of getting exposed to egregious ads resulting in a bad user experience.
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CVPR 20232023We present a simple yet effective self-supervised pretraining method for image harmonization which can leverage large-scale unannotated image datasets. To achieve this goal, we first generate pre-training data online with our Label-Efficient Masked Region Transform (LEMaRT) pipeline. Given an image, LEMaRT generates a foreground mask and then applies a set of transformations to perturb various visual attributes
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CVPR 20232023Recent work leverages the expressive power of generative adversarial networks (GANs) to generate labeled synthetic datasets. These dataset generation methods often require new annotations of synthetic images, which forces practitioners to seek out annotators, curate a set of synthetic images, and ensure the quality of generated labels. We introduce the HandsOff framework, a technique capable of producing
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CVPR Workshop on Safe Artificial Intelligence for All Domains2023Stochastic embedding has several advantages over deterministic embedding, such as the capability of associating uncertainty with the resulting embedding and robustness to noisy data. This is especially useful when the input data has ambiguity (e.g., blurriness or corruption) which often happens with in-the-wild settings. Many existing methods for stochastic embedding are limited by the assumption that the
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CVPR 2023 Workshop on Continual Learning in Computer Vision2023Continual learning enables the incremental training of machine learning models on non-stationary data streams. While academic interest in the topic is high, there is little indication of the use of state-of-the-art continual learning algorithms in practical machine learning deployment. This paper presents Renate, a continual learning library designed to build real-world updating pipelines for PyTorch models
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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.
