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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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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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IEEE APS/URSI 20212021This paper proposes a simulation methodology to optimize MIMO antenna designs for the system performance parameters. The proposed design flow provides a co-simulation bench to evaluate MIMO antenna designs with system-level criteria. In one example, two MIMO antenna designs are considered, where both of them meet conventional antenna design parameters. The proposed methodology identifies the antenna design
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ICPP 20212021Low-precision computation, which has been widely supported in contemporary hardware, is considered as one of the most effective methods to accelerate convolutional neural networks. However, low-precision computation is not widely used to speed up Winograd, an algorithm for fast convolution computation, due to the numerical error introduced by combining Winograd transformation and quantization. In this paper
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Speech Synthesis Workshop2021Artificial speech synthesis has made a great leap in terms of naturalness as recent Text-to-Speech (TTS) systems are capable of producing speech with similar quality to human recordings. However, not all speaking styles are easy to model: highly expressive voices are still challenging even to recent TTS architectures since there seems to be a trade-off between expressiveness in a generated audio and its
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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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