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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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KDD 2021 Workshop on Data-Efficient Machine Learning2021Intelligent Voice Assistant (IVA) systems, such as Alexa, Google Assistant and Siri, allow us to interact with them using just the voice commands. IVAs can elicit voice feedback directly from the users and use their responses to improve the various components of IVAs. One concern with using such crowdsourced voice feedback (CVF) data is the reliability of feedback itself such as background noise or disingenuous
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KDD 2021 Workshop on Data-Efficient Machine Learning2021Automatically evaluating large scale dialogue systems’ response quality is a challenging task in dialogue research. Existing automated turn-level approaches train supervised models on Interaction Quality (IQ) labels or annotations provided by experts, which is costly and time-sensitive. Moreover, the small quantity of annotated data limits the trained model’s ability to generalize to the long tail and out
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ICML 2021 Workshop on Distribution-Free Uncertainty Quantification2021Quantile regression is an effective technique to quantify uncertainty, and fit challenging underlying distributions. Generating full probabilistic predictions requires multiple quantile regressions over multiple quantile levels. As a result, quantile crossing is a common drawback to these approaches since it violates the desirable monotone property of the conditional quantile function. In this work, we
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ICML 2021 Workshop on Machine Learning for Data: Automated Creation, Privacy, Bias, ICLR 20222021While recent automatic data augmentation works lead to state-of-the-art results, their design spaces and the derived data augmentation strategies still incorporate strong human priors. In this work, instead of selecting a set of hand-picked default augmentations alongside the searched data augmentations, we propose a fully automated approach for data augmentation search called Deep AutoAugment (DeepAA).
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IEEE - The Bulletin of the Technical Committee on Data Engineering2021The customer experience of online shopping is largely contingent on the accuracy of product classification. Considering the amount of products and all the possible categories, it is desirable to construct a framework to auto-assign products into correct categories at scale. Machine learning based systems often suffer from poor data quality, such as incomplete item descriptions, adversarial noise in the
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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
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Courtesy of Pai-Ling Yin
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